{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>sales</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.41</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.77</td>\n",
       "      <td>6</td>\n",
       "      <td>247</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.92</td>\n",
       "      <td>0.85</td>\n",
       "      <td>5</td>\n",
       "      <td>259</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.89</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5</td>\n",
       "      <td>224</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.42</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>142</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0                0.38             0.53               2                   157   \n",
       "1                0.80             0.86               5                   262   \n",
       "2                0.11             0.88               7                   272   \n",
       "3                0.72             0.87               5                   223   \n",
       "4                0.37             0.52               2                   159   \n",
       "5                0.41             0.50               2                   153   \n",
       "6                0.10             0.77               6                   247   \n",
       "7                0.92             0.85               5                   259   \n",
       "8                0.89             1.00               5                   224   \n",
       "9                0.42             0.53               2                   142   \n",
       "\n",
       "   time_spend_company  Work_accident  left  promotion_last_5years  sales  \\\n",
       "0                   3              0     1                      0  sales   \n",
       "1                   6              0     1                      0  sales   \n",
       "2                   4              0     1                      0  sales   \n",
       "3                   5              0     1                      0  sales   \n",
       "4                   3              0     1                      0  sales   \n",
       "5                   3              0     1                      0  sales   \n",
       "6                   4              0     1                      0  sales   \n",
       "7                   5              0     1                      0  sales   \n",
       "8                   5              0     1                      0  sales   \n",
       "9                   3              0     1                      0  sales   \n",
       "\n",
       "   salary  \n",
       "0     low  \n",
       "1  medium  \n",
       "2  medium  \n",
       "3     low  \n",
       "4     low  \n",
       "5     low  \n",
       "6     low  \n",
       "7     low  \n",
       "8     low  \n",
       "9     low  "
      ]
     },
     "execution_count": 518,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#satisfaction_level 的分析\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"HR.csv\")\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series([], Name: satisfaction_level, dtype: float64)\n",
      "Empty DataFrame\n",
      "Columns: [satisfaction_level, last_evaluation, number_project, average_montly_hours, time_spend_company, Work_accident, left, promotion_last_5years, sales, salary]\n",
      "Index: []\n",
      "(array([ 195, 1214,  532,  974, 1668, 2146, 1972, 2074, 2220, 2004],\n",
      "      dtype=int64), array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]))\n"
     ]
    }
   ],
   "source": [
    "#先提取出该列数据\n",
    "sl_s = df[\"satisfaction_level\"]\n",
    "# 先看看有没有异常值NaN\n",
    "print(sl_s[sl_s.isnull()])\n",
    "#看一下改行所有数据\n",
    "print(df[df['satisfaction_level'].isnull()])\n",
    "#没有异常值，所以为空\n",
    "#丢弃异常值\n",
    "sl_s = sl_s.dropna()\n",
    "\n",
    "sl_s.mean()     #均值\n",
    "sl_s.std()      #标准差\n",
    "sl_s.quantile(q=0.25)    #下四分位数\n",
    "sl_s.skew()     #偏度\n",
    "sl_s.kurt()     #峰度\n",
    "\n",
    "#获取离散化分布\n",
    "print(np.histogram(sl_s.values,bins=np.arange(0.0,1.1,0.1))) #y的纵坐标表示出现的次顺\n",
    "#输出：(array([ 195, 1214,  532,  974, 1668, 2146, 1973, 2074, 2220, 2004]), array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    14999.000000\n",
      "mean         3.803054\n",
      "std          1.232592\n",
      "min          2.000000\n",
      "25%          3.000000\n",
      "50%          4.000000\n",
      "75%          5.000000\n",
      "max          7.000000\n",
      "Name: number_project, dtype: float64\n",
      "偏度\t 0.3377056123598222 峰度\t -0.4954779519008947\n",
      "4    4365\n",
      "3    4055\n",
      "5    2761\n",
      "2    2388\n",
      "6    1174\n",
      "7     256\n",
      "Name: number_project, dtype: int64\n",
      "4    0.291019\n",
      "3    0.270351\n",
      "5    0.184079\n",
      "2    0.159211\n",
      "6    0.078272\n",
      "7    0.017068\n",
      "Name: number_project, dtype: float64\n",
      "2    0.159211\n",
      "3    0.270351\n",
      "4    0.291019\n",
      "5    0.184079\n",
      "6    0.078272\n",
      "7    0.017068\n",
      "Name: number_project, dtype: float64\n",
      "             satisfaction_level  last_evaluation  number_project  \\\n",
      "sales                                                              \n",
      "IT                     0.618142         0.716830        3.816626   \n",
      "RandD                  0.619822         0.712122        3.853875   \n",
      "accounting             0.582151         0.717718        3.825293   \n",
      "hr                     0.598809         0.708850        3.654939   \n",
      "management             0.621349         0.724000        3.860317   \n",
      "marketing              0.618601         0.715886        3.687646   \n",
      "product_mng            0.619634         0.714756        3.807095   \n",
      "sales                  0.614447         0.709717        3.776329   \n",
      "support                0.618300         0.723109        3.803948   \n",
      "technical              0.607897         0.721099        3.877941   \n",
      "\n",
      "             average_montly_hours  time_spend_company  Work_accident  \\\n",
      "sales                                                                  \n",
      "IT                     202.215974            3.468623       0.133659   \n",
      "RandD                  200.800508            3.367217       0.170267   \n",
      "accounting             201.162973            3.522816       0.125163   \n",
      "hr                     198.684709            3.355886       0.120433   \n",
      "management             201.249206            4.303175       0.163492   \n",
      "marketing              199.385781            3.569930       0.160839   \n",
      "product_mng            199.965632            3.475610       0.146341   \n",
      "sales                  200.911353            3.534058       0.141787   \n",
      "support                200.758188            3.393001       0.154778   \n",
      "technical              202.497426            3.411397       0.140074   \n",
      "\n",
      "                 left  promotion_last_5years  \n",
      "sales                                         \n",
      "IT           0.222494               0.002445  \n",
      "RandD        0.153748               0.034307  \n",
      "accounting   0.265971               0.018253  \n",
      "hr           0.290934               0.020298  \n",
      "management   0.144444               0.109524  \n",
      "marketing    0.236597               0.050117  \n",
      "product_mng  0.219512               0.000000  \n",
      "sales        0.244928               0.024155  \n",
      "support      0.248991               0.008973  \n",
      "technical    0.256250               0.010294  \n",
      "_________________________________\n",
      "             last_evaluation\n",
      "sales                       \n",
      "IT                  0.716830\n",
      "RandD               0.712122\n",
      "accounting          0.717718\n",
      "hr                  0.708850\n",
      "management          0.724000\n",
      "marketing           0.715886\n",
      "product_mng         0.714756\n",
      "sales               0.709717\n",
      "support             0.723109\n",
      "technical           0.721099\n",
      "_________________________________\n",
      "sales\n",
      "IT             212\n",
      "RandD          210\n",
      "accounting     213\n",
      "hr             212\n",
      "management     210\n",
      "marketing      214\n",
      "product_mng    212\n",
      "sales          214\n",
      "support        214\n",
      "technical      213\n",
      "Name: average_montly_hours, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#number_project 的分析\n",
    "#静态结构分析\n",
    "#先提取出该列数据\n",
    "np_s = df[\"number_project\"]\n",
    "print(np_s.describe())\n",
    "print(\"偏度\\t\",np_s.skew(),\"峰度\\t\",np_s.kurt())\n",
    "\n",
    "#计算样本出现次数\n",
    "print(np_s.value_counts())\n",
    "#获得构成、比例\n",
    "print(np_s.value_counts(normalize=True))\n",
    "#排序\n",
    "print(np_s.value_counts(normalize=True).sort_index())\n",
    "#简单对比分析操作\n",
    "#先剔除异常值\n",
    "#剔除空值 axis=0表示行，1表示列， how=any表示任意为空\n",
    "df = df.dropna(axis=0,how=\"any\")\n",
    "df = df[df[\"last_evaluation\"]<=1][df[\"salary\"]!=\"nme\"][df[\"sales\"]!=\"sale\"]\n",
    "\n",
    "#以部门为单位进行简单对比分析\n",
    "print(df.groupby(\"sales\").mean())\n",
    "print(\"_________________________________\")\n",
    "\n",
    "#单独拉出某一列来分析\n",
    "print(df.loc[:,[\"last_evaluation\",\"sales\"]].groupby(\"sales\").mean())\n",
    "#自己定义函数进行对比, 计算极差\n",
    "print(\"_________________________________\")\n",
    "\n",
    "import math\n",
    "print(df.loc[:,[\"average_montly_hours\",\"sales\"]].groupby(\"sales\")[\"average_montly_hours\"].apply(lambda x:x.max()-x.min()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 可视化分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>sales</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.41</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.77</td>\n",
       "      <td>6</td>\n",
       "      <td>247</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.92</td>\n",
       "      <td>0.85</td>\n",
       "      <td>5</td>\n",
       "      <td>259</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.89</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5</td>\n",
       "      <td>224</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.42</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>142</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0                0.38             0.53               2                   157   \n",
       "1                0.80             0.86               5                   262   \n",
       "2                0.11             0.88               7                   272   \n",
       "3                0.72             0.87               5                   223   \n",
       "4                0.37             0.52               2                   159   \n",
       "5                0.41             0.50               2                   153   \n",
       "6                0.10             0.77               6                   247   \n",
       "7                0.92             0.85               5                   259   \n",
       "8                0.89             1.00               5                   224   \n",
       "9                0.42             0.53               2                   142   \n",
       "\n",
       "   time_spend_company  Work_accident  left  promotion_last_5years  sales  \\\n",
       "0                   3              0     1                      0  sales   \n",
       "1                   6              0     1                      0  sales   \n",
       "2                   4              0     1                      0  sales   \n",
       "3                   5              0     1                      0  sales   \n",
       "4                   3              0     1                      0  sales   \n",
       "5                   3              0     1                      0  sales   \n",
       "6                   4              0     1                      0  sales   \n",
       "7                   5              0     1                      0  sales   \n",
       "8                   5              0     1                      0  sales   \n",
       "9                   3              0     1                      0  sales   \n",
       "\n",
       "   salary  \n",
       "0     low  \n",
       "1  medium  \n",
       "2  medium  \n",
       "3     low  \n",
       "4     low  \n",
       "5     low  \n",
       "6     low  \n",
       "7     low  \n",
       "8     low  \n",
       "9     low  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " #柱状图\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "df = pd.read_csv('HR.csv')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(np.arange(len(df[\"salary\"].value_counts())),df[\"salary\"].value_counts())\n",
    "plt.title('SNLARY')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26541fe56a0>"
      ]
     },
     "execution_count": 527,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 设置样式\n",
    "sns.set_style(style='whitegrid') # 背景\n",
    "sns.set_context(context='poster', font_scale=0.8) # 文本大小\n",
    "#sns.set_palette('summer')# 色系\n",
    "sns.set_palette([sns.color_palette('RdBu', n_colors=7)[5]])\n",
    "# 柱状图\n",
    "# plt\n",
    "plt.title('salary')\n",
    "plt.xlabel('salary')\n",
    "plt.ylabel('number')\n",
    "# 标注\n",
    "plt.xticks(np.arange(len(df['salary'].value_counts())), df['salary'].value_counts().index)\n",
    "# xmin,xmax,ymin,ymax\n",
    "plt.axis([0,4,0,10000])\n",
    "plt.bar(np.arange(len(df['salary'].value_counts()))+0.5, df['salary'].value_counts(), width=0.5)\n",
    "# 添加值标记\n",
    "for x,y in zip(np.arange(len(df['salary'].value_counts()))+0.5, df['salary'].value_counts()):\n",
    "    plt.text(x,y,y,ha='center',va='bottom')\n",
    "plt.show()\n",
    "# sns\n",
    "sns.countplot(x='salary', hue='sales', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anacondainstall\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 直方图\n",
    "f = plt.figure()\n",
    "f.add_subplot(1,3,1) # 一行三列\n",
    "sns.distplot(df['satisfaction_level'], bins=10)# kde和hist控制曲线和直方\n",
    "f.add_subplot(1,3,2)\n",
    "sns.distplot(df['last_evaluation'], bins=10)\n",
    "f.add_subplot(1,3,3)\n",
    "sns.distplot(df['average_montly_hours'], bins=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 箱线图\n",
    "sns.boxplot(x=df['time_spend_company'], saturation=0.75, whis=3) # whis控制k\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x265468aceb8>"
      ]
     },
     "execution_count": 531,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 折线图\n",
    "sub_df = df.groupby('time_spend_company').mean()\n",
    "sns.pointplot(sub_df.index, sub_df['left'])\n",
    "plt.show()\n",
    "sns.pointplot(x='time_spend_company', y='left', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 饼图\n",
    "lbs = df['sales'].value_counts().index\n",
    "explodes = [0.1 if i == 'sales' else 0 for i in lbs] # 突出强调\n",
    "plt.pie(df['sales'].value_counts(normalize=True), explode=explodes, labels=lbs, autopct='%1.1f%%', colors=sns.color_palette('Reds'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据的获取和探索性分析--多因子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.stats as ss\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_dist=ss.norm.rvs(size=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.16263579,  0.59737839,  0.14882519,  1.33425451, -0.30757446,\n",
       "       -0.91603357, -0.56405534,  1.11167226,  0.13182261,  0.39308801,\n",
       "       -1.26540914,  0.48212311, -0.46004997,  0.4171423 ,  2.15818691,\n",
       "        0.78800868,  1.07087491,  0.68960504,  0.24516572,  1.04450826])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=0.21812534694399277, pvalue=0.8966742180506517)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss.normaltest(norm_dist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "卡方检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(126.08080808080808, 2.9521414005078985e-29, 1, array([[55., 55.],\n",
       "        [45., 45.]]))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss.chi2_contingency([[15,95],[85,5]])#男女化妆的例子"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "独立t分布检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=0.3794420587956506, pvalue=0.7072222193704272)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss.ttest_ind(ss.norm.rvs(size=10),ss.norm.rvs(size=20))#即p值偏小，所以两者符合原假设(两个正态分布)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "方差检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=17.619417475728156, pvalue=0.0002687153079821641)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss.f_oneway([49,50,39,40,43],[28,32,30,26,34],[38,40,45,42,48])#电池的例子"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "相关系数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1=pd.Series([11,23,433,5,67,343,52])\n",
    "s2=pd.Series([45,45,34,545,656,34,34])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.3729434911354079"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.corr(s2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.5426488617097118"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.corr(s2,method=\"spearman\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.arange(10).astype(np.float).reshape((10,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=x*3+4*np.random.random((10,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [3.],\n",
       "       [4.],\n",
       "       [5.],\n",
       "       [6.],\n",
       "       [7.],\n",
       "       [8.],\n",
       "       [9.]])"
      ]
     },
     "execution_count": 543,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 544,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.52490077],\n",
       "       [ 6.31963902],\n",
       "       [ 6.30995512],\n",
       "       [11.37857349],\n",
       "       [13.11594833],\n",
       "       [17.5497295 ],\n",
       "       [18.98424342],\n",
       "       [21.24451027],\n",
       "       [25.30623691],\n",
       "       [27.38960587]])"
      ]
     },
     "execution_count": 544,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 547,
   "metadata": {},
   "outputs": [],
   "source": [
    "res=reg.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=res.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.85018615],\n",
       "       [ 5.57510795],\n",
       "       [ 8.30002976],\n",
       "       [11.02495156],\n",
       "       [13.74987337],\n",
       "       [16.47479517],\n",
       "       [19.19971698],\n",
       "       [21.92463878],\n",
       "       [24.64956059],\n",
       "       [27.37448239]])"
      ]
     },
     "execution_count": 549,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 550,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.85018615])"
      ]
     },
     "execution_count": 550,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.coef_#参数\n",
    "res.intercept_#截距"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 主成分分析（PCA）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2.5 2.4]\n",
      " [0.5 0.7]\n",
      " [2.2 2.9]\n",
      " [1.9 2.2]\n",
      " [3.1 3. ]\n",
      " [2.3 2.7]\n",
      " [2.  1.6]\n",
      " [1.  1.1]\n",
      " [1.5 1.6]\n",
      " [1.1 0.9]]\n",
      "降维后信息量： [0.96318131] \n",
      "转换后：  [[-0.82797019]\n",
      " [ 1.77758033]\n",
      " [-0.99219749]\n",
      " [-0.27421042]\n",
      " [-1.67580142]\n",
      " [-0.9129491 ]\n",
      " [ 0.09910944]\n",
      " [ 1.14457216]\n",
      " [ 0.43804614]\n",
      " [ 1.22382056]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import numpy as np\n",
    "data = np.array([np.array([2.5,0.5,2.2,1.9,3.1,2.3,2,1,1.5,1.1]),np.array([2.4,0.7,2.9,2.2,3,2.7,1.6,1.1,1.6,0.9])]).T\n",
    "print(daata)\n",
    "#注：sklearn里用PCA的是奇异值分解\n",
    "from sklearn.decomposition import PCA\n",
    "lower_dim = PCA(n_components=1)\n",
    "lower_dim.fit(data)\n",
    "print(\"降维后信息量：\",lower_dim.explained_variance_ratio_,\"\\n转换后： \",lower_dim.fit_transform(data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 复合分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=-1.0601649378624074, pvalue=0.2891069046174478)\n",
      "['IT', 'RandD', 'accounting', 'hr', 'management', 'marketing', 'product_mng', 'sales', 'support', 'technical']\n",
      "[[ 1.         -1.         -1.         -1.         -1.          0.45049248\n",
      "   0.8699759   0.10603064  0.08079527 -1.        ]\n",
      " [-1.          1.         -1.         -1.          0.62589651 -1.\n",
      "  -1.         -1.         -1.         -1.        ]\n",
      " [-1.         -1.          1.          0.28014632 -1.          0.17267179\n",
      "  -1.          0.2153416   0.35115835  0.58712105]\n",
      " [-1.         -1.          0.28014632  1.         -1.         -1.\n",
      "  -1.         -1.         -1.          0.05777944]\n",
      " [-1.          0.62589651 -1.         -1.          1.         -1.\n",
      "  -1.         -1.         -1.         -1.        ]\n",
      " [ 0.45049248 -1.          0.17267179 -1.         -1.          1.\n",
      "   0.39331946  0.60491791  0.47370349  0.24747714]\n",
      " [ 0.8699759  -1.         -1.         -1.         -1.          0.39331946\n",
      "   1.          0.10556601  0.08053988 -1.        ]\n",
      " [ 0.10603064 -1.          0.2153416  -1.         -1.          0.60491791\n",
      "   0.10556601  1.          0.71969859  0.2891069 ]\n",
      " [ 0.08079527 -1.          0.35115835 -1.         -1.          0.47370349\n",
      "   0.08053988  0.71969859  1.          0.55898662]\n",
      " [-1.         -1.          0.58712105  0.05777944 -1.          0.24747714\n",
      "  -1.          0.2891069   0.55898662  1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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JYyPiqVyz+T5wd6vO/U5SVR9P167HUyGvx2MjredfDw27KnLba/ds6zNnwl9+35HVnk5sKPyopFmkWsk40ig3MzPLekNtPTpVxyWeiDgpD2GeEBEfiIhnRvqazMw6SZHDqSXtIelOSfdIOrzF6y+SdGZ+/fqGPvjl1nGJx8zMBra4V209BiNpNKlL4+2kW1oOlDShabcPA09ExOuAk4Djh3v9TjxmZjVTYI1nW+CeiJgfEf8CfsWyA6/2Bk7Nz88G3tI3unh5OfGYmdVMgaPaXkm6ob/Pg7ms5T4R0UMaafzS4Vy/Z6cuwIIv7lhJnCpHmt0/6fWVxVp/5l2VxapqtFlVo+cAFn31sMpirbTlGyqL1fvAQ4PvVJBF1z9YWawitDtwIM8OM7WhaFqedeWFXVoc1pyy2tlnSJx4zMxqpt2BA41Te/XjQeDVDduvAh7uZ58HJa1EGm38eNsX24Kb2szMaqbA4dQ3AhtJ2kDSKsD7gPOa9jkPOCg/35c0WbNrPGZmK5LFBd2jExE9kg4hTUk2GjglIuZJOgaYGRHnAT8FTpN0D6mm877hxnXiMTOrmSKXPIiIC4ELm8qOani+CNivsIA48ZiZ1U7NV0Vw4jEzq5uo+eottRpcIGly31LYbe4/UdI7Grbf1WpKCDOzOumN9h6dqjY1njyMb6gmApPI7Ze5o6x5xIaZWa0srledYRmlX72k8ZLukHSypFslnS5pd0lXS7pb0rb5cY2kW/LPjfOxUySdJel84OKm826T999Q0mqSTpF0Yy7bOw8NPAY4QNIsSQfk830vHz9d0ndyvPmS9s3loyT9QNI8SRdIurDvNTOzTtDb5qNTVZU2Xwd8G9gC2AR4P7ATafnr/yatPLpLRGwFHAV8veHYHYCDIuLNfQWS3gT8CNg7IuYDR5DGlm8D7AZ8k7Ro3FHAmXm26zNbXNe6+TreCRyXy94LjAc2Bz6S45uZdYxAbT06VVWJ596ImBsRvaR1di7NNyDNJX3IjwPOyiuLngRs2nDsJRHReJfsG0h34u4VEffnsrcBh+d1fGYAY4D127iucyKiNyJuA9bJZTsBZ+Xy/wMua3WgpKmSZkqaecrN89sIZWZWDNd42vNcw/Pehu1eUj/TscBlEbEZsBcpcfR5uulcjwCLgK0aygTsk2s2EyNi/Yi4fYjXpaafA4qIaRExKSImHfzGDds5xMysEE48xRgH9M0IOGWQfZ8E9gS+LmlyLrsI+HTfVN2S+pLSQmD1IV7LVcA+ua9nHWDyIPubmVXKTW3FOAH4hqSrSdM2DCgiHiXVjL4vaTtSjWllYE5urjs273oZMKFvcEGb1/Ib0qR4t5KW3b6eNA24mVlH6JHaenSq0odTR8R9wGYN21P6ea1xHv4j8+vTgekN+88g9eGQ+3ca+4I+1iL248A2TcXTm68jb4/NP3slfT4inpL0UuAGUl+UmVlH6OBbdNpSm/t4KnaBpDWBVYBj8yADM7OO0Mn9N+1w4mkhIiaP9DWYmfWnt4Ob0drhxGNmVjNuajMzs0q5qc3MzCrVySPW2uHEY2ZWM25qM1Y64KPVBDr+6mriAOvPvKuyWN1o0VcPqyzWmP/5dmWx7p/8icpi9fRUd5vhpvPvqSxWTwHn6K13hceJx8ysbtzHY2ZmlXJTm5mZVarHTW1mZlYlN7WZmVmlwjUeMzOrkms8ZmZWqbonnsrX45E0Pq+ZszzHTpb0pqKvycysTqLNR6cqrMYjaXRELC7qfP2YDDwFXFNyHDOzjlX3UW1t1XhyLeUOSadKmiPpbEkvlnSfpKMkXQXsJ2mipOvyPr+TtFY+fmtJsyVdC3yq4bxTJH2vYfuCvuWsJe0h6eZ83KWSxgMfBz6bVxTduZ9rnS7ph5IukzRf0q6STpF0u6TpDfs9Jelr+fzX5WWukfTavH2jpGMkPTW0X6mZWbl623x0qqE0tW0MTIuILYB/Ap/M5YsiYqeI+BXwc+CLeZ+5wJfzPj8DDo2IHdoJJGlt4CfAPhGxJbBfXq30R8BJETExIq4c4BRrAW8GPgucD5xEWq10c0kT8z6rAdfl818B9M17823g2xGxDfDwANc4VdJMSTNPPuv37bwtM7NC1L2pbSiJ54GI6Jss7BfATvn5mQCSxgFrRsTlufxUYJcW5ae1EWt74IqIuBdeWMJ6KM6PiCAlv0cjYm5E9ALzgPF5n38BF+TnNzWU7wCclZ+f0V+AiJgWEZMiYtJH9ttziJdnZrb8etXeo1MNpY+nOYH2bT89yHFqcWyfHpZOfmPaOKYdz+WfvQ3P+7b73vPzOTkBLMYj/MysJjq5Ga0dQ6nxrC+pr6nsQOCqxhcjYgHwREPfyweByyPiSWCBpL4a0gcaDrsPmChplKRXA9vm8muBXSVtACDpJbl8IbD6EK55eVwH7JOfv6/kWGZmQ7YiNbXdDhwkaQ7wEuCHLfY5CPhm3mcicEwu/xDw/Ty44NmG/a8G7iU1iZ0I3AwQEX8HpgK/lTSb3JxH6q95z0CDCwrwGeBzkm4A1gUWlBTHzGy59BBtPTrVUJqXeiPi401l4xs3ImIWqX+GpvKbgC0bio7O5cHSNaDGY/4A/KGp7C5gi4EuMiKmNDy/D9isn9fGNjw/Gzg7bz4EbB8RIel9wMyB4pmZVa1zU0p73K+xrK2B70kS8CRw8Ahfj5nZUurex9NW4mmuOXQCSUcA+zUVnxURXxvOefMw7S0H3dHMbIRUNWIt96+fSWrdug/YPyKe6GffNUhdMr+LiEMGOm/lU+YUJSK+lu/naXwMK+mYmdVBL9HWowCHA5dGxEbApXm7P8cClw/w+gtqm3jMzFZUi9t8FGBv0j2Z5J/vbrWTpK2BdYCL2zmp+3gK8OD7ThzpSyjcwzu/rrJY6115T2WxqrLSlm+oLNb9kz9RWaz1Z7QazFqOnhsvGHyngjw2fVFlsYpQUG2mHetExCMAEfGIpJc37yBpFPAt0i00b2nnpE48ZmY1027akTSVdGtKn2kRMa1pnz8Br2hx+BFthvkkcGFEPJDGZA3OicfMrGbaHdWWk8y0QfbZvb/XJD0qad1c21kX+FuL3XYAdpb0SWAssIqkpyKi3/4gJx4zs5qpsKntPNLEAMfln+c27xARL9yLKWkKMGmgpAMeXGBmVjsVTplzHPBWSXcDb83bSJok6eTlPalrPGZmNbO4ohpPRPyDFgMGImIm8JEW5dOB6YOd14nHzKxmVoiZC8zMrHNU2MdTihWujycvjb3vSF+HmdnyqvuyCK7xmJnVTN1rPF2ReCStBvwaeBUwmjRn0MbAXsCqwDXAxxpWHO07bmvgf0ljzx8DpuTx6ocCHyetkHpbRHhBODPrGFUNLihLVyQeYA/g4YjYE0DSOOCSiDgmb58GvJO0kBy5bGXgu8DeEfF3SQcAXyMtg3A4sEFEPCdpzWrfipnZwOo+uKBb+njmArtLOl7SznkZ7t0kXS9pLvBmYNOmYzYmLfVwiaRZwP+QakwAc4DTJf07qdazDElTJc2UNPNXjz9YxnsyM2sp2vyvU3VFjSci7srNZu8AviHpYuBTpDtoH5B0NDCm6TAB8yJihxan3BPYBXgXcKSkTSNiqQTUOBXFXzb7t879FzazruMaTweQtB7wTET8AjgReGN+6TFJY4FWo9juBNaWtEM+x8qSNs0zrb46Ii4D/gtYk9QHZGbWEXoj2np0qq6o8QCbA9+U1As8D3yCtG7EXNKqeTc2HxAR/8rDqr+T+4RWAv4fcBfwi1wm4KSIeLKSd2Fm1obOTSnt6YrEExEXARc1Fc8k9ds07zul4fksUpNas52KvD4zsyItrnljW1ckHjOzFUm9044Tj5lZ7fgGUjMzq1QnD5VuhxOPmVnNuKnNzMwqFR08VLodTjwF6OnpituhljJmwxdVF+zK6kJVpfeBhyqLVeXfX8+NF1QWa6Vt3llZrJh/V2WxitDjpjYzM6uS+3jMzKxSHtVmZmaVch+PmZlVyqPazMysUp4yx8zMKuWmNjMzq1TdBxd03w0oBZH0GUkvHunrMDNrVvcVSJ14WpA0GvgM4MRjZh2n7gvBdVzikbSapN9Lmi3pVkkHSLpP0svy65MkzcjPj5Z0mqQ/S7pb0kdz+WRJV0j6naTbJP0oryyKpAMlzc3nPr4h7lOSjpF0PXAEsB5wmaTLqv4dmJkNJNp8dKqOSzzAHsDDEbFlRGwG/HGQ/bcA9gR2AI7Ky2ADbAv8J2l10tcC782vHQ+8GZgIbCPp3Xn/1YBbI2K7iDgGeBjYLSJ2axVU0lRJMyXNPPPJB5b7zZqZDVUPvW09OlUnJp65wO6Sjpe0c0QsGGT/cyPi2Yh4DLiMlHAAboiI+RGxGPglaVXRbYAZEfH3iOgBTmfJCqSLgd+0e5ERMS0iJkXEpAPWfPUQ3p6Z2fBERFuPTtVxo9oi4i5JWwPvAL4h6WKghyVJckzzIf1styrXAKEX5SRlZtbRPKqtYLk57JmI+AVwIvBG4D5g67zLPk2H7C1pjKSXApOBG3P5tpI2yH07BwBXAdcDu0p6WR5AcCBweT+XshBYvZh3ZWZWnLqPauu4Gg+pT+abknqB54FPAKsCP5X036Tk0egG4PfA+sCxEfGwpNcD1wLH5fNdAfwuInolfYnUJCfgwog4t5/rmAb8QdIj/fXzmJmNhE5uRmtHxyWeiLgIuKjFS6/v55C7ImJqi/JnIuKAFuc/AzijRfnYpu3vAt8d/IrNzKpV96a2jks8ZmY2sMXRuSPW2lHrxBMRR/dTPgOYUeW1mJlVpar+G0kvAc4ExpP62vePiCda7HcC6baWUcAlwGExQHtgxw0uMDOzgVU4c8HhwKURsRFwad5eiqQ3ATuS7qncjHTbyq4DndSJx8ysZioc1bY3cGp+firw7hb7BOk2l1WAFwErA48OdNJaN7WZma2IKpyHbZ2IeAQgIh6R9PLmHSLi2jy12COk0cLfi4jbBzqpE08BNp0/Z6QvoXArb7dZZbHm/HnlymJt8cCsSuIsuv7BSuIAbDr/nspiPTZ9UWWxYv5dlcVa+YDPVRarCO0OLpA0FWgc9TstIqY17fMn4BUtDj+izRivA94AvCoXXSJpl4i4or9jnHjMzGqm3Wa0nGSmDbLP7v29JulRSevm2s66wN9a7PYe4LqIeCof8wdge9L9ky25j8fMrGYqHFxwHnBQfn4Q0OqG+/tJM8KsJGll0sCCAZvanHjMzGqmwsEFxwFvlXQ38Na83bc8zcl5n7OBv5AmeJ4NzI6I8wc6qZvazMxqJiq6gTQi/gG8pUX5TOAj+fli4GNDOa8Tj5lZzXjKHDMzq1Tdp8wZkT4eSWtK+uRyHjtd0r5DPOZkSROWI9ZkSRcM9TgzszLVfSG4kRpcsCawXIlneUTERyLitqrimZmVqcJRbaUYqcRzHPBaSbMkfVPSFyTdKGmOpK/07STpP3LZbEmnNRy/i6RrJM3vq/3k2skMSWdLukPS6ZKUX5shaVJ+voekm/M5L81l2+bz3ZJ/blzdr8LMbGi8ENzyORzYLCImSnobsC+wLWm6hfMk7QL8g3Tn7I4R8VieJbXPusBOwCakceZn5/KtgE2Bh4GrSRPXXdV3kKS1gZ8Au0TEvQ3nvCOX9UjaHfg6y650ambWETq5Ga0dnTC44G35cUveHgtsBGwJnB0RjwFExOMNx5wTaTzhbZLWaSi/ISIeBJA0izSV91UNr28PXBER9zadcxxwqqSNSBPeDTqHS+NUFBo9jlGjVmv7DZuZDYdHtQ2fgG9ExI+XKpQOhX5/u881Hd+qfDHLvj/1c85jgcsi4j2SxtPGWj6NU1GstMor6/1XYGa1srjXo9qWx0Jg9fz8IuBgSWMBJL0yz4B6KbC/pJfm8pe0PNPQXEua2mGDpnOOAx7Kz6cUEMfMrDR1H9U2IjWeiPiHpKsl3Qr8ATgDuDaPBXgK+PeImCfpa8DlkhaTmuKmDDPu33MT2W8ljSJNePdW4ARSU9vngD8PJ4aZWdnc1LacIuL9TUXfbrHPqSxZhKivbErT9tj8cwYNTWQRcUjD88kNz/9ASnaN57gWeH1D0ZGtzmlm1gk6uTbTjk7o4zEzsyHo5Ht02uHEY2ZWM3WfMsdLgnaBAAATs0lEQVSJx8ysZtzUZmZmlerkWQna4cRjZlYzrvGYmVml6p54VPc3UFeSpubZDxyrw2N143tyrPrE6UYjNXOB5XneHKsWsbrxPTlWfeJ0HSceMzOrlBOPmZlVyoln5FTZNuxY9YjjWPWK5f6d5eTBBWZmVinXeMzMrFJOPGZmViknHjMzq5QTTwUkXTzS19ANJI2W9NmKYh3WTllBsd7b4vGWvBJv7UkaJWmNEs9/aTtl1jk8uKACkm6JiK0qjLcb8Glg41x0O/C9vLBdGfE+16J4AXBTRMwqONaMxoX9yiLp5oh4Y1NZKf+Okn4P7ABclosmA9eRFic8JiJOKzDWQlhmhskFwEzgPyNifkFxzgA+DiwGbiItL/+/EfHNIs6fY4wBXkz6vU0GlF9aA/hDRLyhwFjns+zv7QUR8a6iYq0IPFdbNcZJem9/L0bEb4sKJGlP4HvAMcBXSP8zvhE4RdIhEXFhUbEaTMqP8/P2nsCNwMclnRURJxQY62pJ3wPOBJ7uK4yIm4s4uaQDgfcDG0g6r+Gl1YF/FBGjhV7gDRHxaL6GdYAfAtsBVwCFJR7gf4GHScvNC3gf8ArgTuAU0gd4ESZExD8lfQC4EPgiKQEVlniAjwGfAdbL5+5LPP8Evl9gHIATCz7fCs01ngpI+gdwLkv+x2gUEXFwgbFmAIdFxOym8i2A70bErkXFajj3RcA+EfFU3h4LnA28h1TrmVBgrL5awVJ/uBHx5oLO/xpgA+AbwOENLy0E5kRETxFxmmLOjYjNG7YFzI2IzYquZUm6PiK2ayq7LiK2lzQ7IrYsKM48YCIpwX0vIi4v8vwNcUYD/x0RxxZ5XiuXazzV+GuRyWUQr2hOOgARMSd/ky7D+sC/GrafB14TEc9Keq7gWG8H9gHGs+Tvt7BvTxHxV+CvpKavqlwp6QLgrLy9D3CFpNWAJwuO1Stpf9IXA4B9G14r8lvoj4H7gNmk9/IaUk2kUBGxWNI7gEoSj6SNSF9KJgBjGq5jwyridwsnnmq0qumU5enlfG04zgCuk3Ru3t4L+GX+4Lyt4FjnkD6MbwYW5bLCq+25afR44OWkfz+RaqdldJJ/ipRsdsxxfg78JlJzxG4Fx/oA8G3gB6Tf23XAv0taFTikqCAR8R3gOw1Ff819j2W4WNI+wG+j/CacnwFfBk4i/dt8iGr//+4KbmqrgKRNI2JeRbGeJPULLPMSsFNErFVS3Eks+eC8KiJmlhTn1ojYrIxzN8W5B9grIm4vO1Y3yrXrrwPrRcTbJU0AdoiIn5YQayGwGmkgw7OU+CVB0k0RsXVj86ikKyNi56JjdTPXeKpxnaRWGb6M/0H2HuC1MjtIbyF1Wq8EIGn9iLi/hDjXSNo8IuaWcO5Gj1aVdKqsXUlaG/goSzdVUkJT8HRS7eCIvH0XaUBI4YknIlYv+pwDWCRpFHC3pEOAh0j/bjYErvHYsEn6NKn54VHSt86+D84tCowxl9Q0tBKwETAfeK6MWDnet0mjvc7JcYBiRyA2xKqsdiXpGuBK0iiwxX3lEfGbguPcGBHbNA6OkDQrIiYWGach3ruAXfLmjIi4oKQ425BuT1iT1K+0BvDNiLiujHjdyjWeLtPwAd1S0R/Q2WHAxhFR1nBjgHeWeO5W1gCeAd7WUBZA4YmHCmtXwIsj4osVxHla0kvJf4uStifdL1Q4SccB2wCn56LDJO0UEYcPcNhyiYgb89OnSP07thxc4+kyefQQpA5rWHIPyAeAZyLimBJiXga8tYyhxiuCimtXXwWuKel+rsY4bwS+C2wG3AqsDewbEXNKiDUHmBgRvXl7NHBLGV+yJF0C7BcRT+bttYBfRcS/FR2rmznxdClJV0fEjoOVFRTrp6RZEn7P0h+c/1t0rKpIej3pJs518v00WwDvioivlhDrZy2KC72/qyFWX0f8c6Rh72X2J61E+rsQcGdEPF90jBxnDjA5Ih7P2y8hNbeVkXiWua+q6plJuoGb2rrXarm54SoASW8ifeCU4f78WCU/usFPgC+Q7kfpuw/qDKDwxBMRlTXZlN0RP8AMHa+XVEotjnRfzS255i1SX8+XSogD6T6oFwbO5BYGf3sfIiee7vVh0jQ54/L2k0ApN7FGxFfKOO8Ie3FE3JAmEXhBoU2Jkv4rIk6Q9F1afHhFxKFFxmuIuwXLjmorKiHsNcBrpfSRRcQv84wd2+SiL0bE/xUdJzsCuErS5Xl7F2BqSbG6lhNPl4qIm4At86zAiojCO3Yl/b+I+Ex/EyjWfOLExyS9liWd4/sCjxQco29AQSn3PLUi6RRgC2AeaY44KDAhVFl7a7IDsBPpvYwGfldGkIj4Y+6/2p5Uu/psRDxWRqxu5j6eLiXpRSw7tQxFDi6QtHVE3CSp5fxvEXF5q/I6kLQhMA14E/AEcC/w7xFxXwmx9ouIswYrKyjWbUXOnTdIrD2BTVl6apkyBrf8AHgd8MtcdADwl4j4VP9HDTnGJhFxR046y4iCJqldUTjxdClJfyQvTcDS92t8q4RYh0XEtwcrq6M87c+oiFhYYoxWSzAsU1ZQrJ8C34qIoqcyao7zI9KSBbsBJ5PmhLshIj5cQqx5wGZ90+XkGzznRsSmBcaYFhFTGyapbRRR0CS1Kwonni5V1dQyOVZla9dURdKawH+wbI2xsH4XSW8H3gHsT7qrv88apGUFti0qVkPMXUjLV/wf5d6AOycitmj4OZY0l9rbBj146LF+S2ry+mvefg1wXEQcWHQsK4b7eLpX6VPLaGTWrqnKhaQJNOeypC+kaA+T+nfeRaqZ9lkIlLXS6inAByn3fcGSCVyfkbQe8DhpuYkyvBS4XdINeXsb4Nq+v8mi+xrzCNHxLP2F5OdFxuh2TjzdaydgiqR7Ke+b7TWkDveXAY1NeAuBwm8UrNiYiGi1smphIi1fMTsP014JWD8i7iwzJnB/RJw3+G7Ddn6uNX6TNJN4kIaol+Goks67DEmnAa8FZrGkCTtIM4pbm9zU1qUaZjBYSl9zhA1M0mdJ06JcwNI3xT5eQqy9SBO4rhIRG0iaSFryuvBRgbkjfk1Sc1tpsyRI2g/4Y0QslHQkaRXcY8vqhJf0CmBbUhK4sazh1JJuJzWD+oNzGEaN9AVYOSLirznJPEv6n7HvUThJ75V0t6QFkv4paaGkwhf9qti/SN/WryU1g91EecOejyZ9aD4JEBGzSE05ZViVlHDeRrrnZi/KmQfvyJx0dgLeSpqt+oclxEHSR4AbgPeSBjFcJ6mshRdvJU1vZMPgprYulWfr/RZpPfq/Aa8h3TdS2EifBifQfWvXfA54XUX3aPRExIKmm1VLMdh9NpK+FBHfKCBUXzPUnsCPIuJcSUcXcN5WvgBs1TdJbZ6c9BpSf1bRXgbclvuTGmuMdb5nrXJOPN3rWNJNbn+KiK2UVn8sa5RPlbMrV2UeaXbqKtwq6f3AaKWllQ8lfXCOhP1IU9AM10OSfgzsDhyf7ysrq4XlQVK/Yp+FwAMlxTq6pPOuUNzH06UkzYyISZJmk74N9kq6oaQhupXNrlwVSb8j1Q4vY+n3VPg0NpJeTJqKpW+o8cWk/pBF/R9VjqKGwef3tAfpfpq7Ja0LbB4RFw/7IpeN9XNgc+BcUnPy3qSmt7ug3pPVdivXeLrXk/neiSuA0yX9jYLnGmtQ5do1VTknP6rw8og4giWrdfYtOHZj/4eUppBvohHxDA3//hHxCMVPOdTnL/nR59z8s/AJUVXharHdzDWeLpXvuH+W1LzxAWAccHqUu1hbV5G0KhUMcZZ0M6mP7KG8vQvw/YjYvMy4/VxLrW/8LZsqXC22m7nG06Ui4un8tBc4VWlxrPexZJXGwuT1ZFpNElrWyKLSNQ5xJt0gW9oQZ+BjwDk55huBr5NmNCicpB0j4uoBygqfH65seRqbVn9/ZUxj0439mZVzjafL5NmoPwW8EjgPuCRvfwGYFRF7lxBzn4bNMcB7gIfLmta/CpJuAt5MWlBsq1w2t6xaiKQdSGv/LAL2jIi/lxSnsnnhqiJp64bNMaTJcXsi4r8KjNG3ztCudFl/5khwjaf7nEaaTfla4COkhLMKsHe+P6RwEfGbxm1JvwT+VEasCrUa4lzot7QWy0m8mDSx60+VFk0rrHaVE9ubgLUlNc7IsAZpGYHaykuANLpaS9bLKUrjOkPd1p9ZOSee7rNh37dySScDj5H6KUqbXbmFjYD1K4xXhiqGOJ9Y8PkGsgowlvT/fGOn+z9JN13WltJS131GAZMo+CbPEVxnqCu5qa3LNDebVNGMImkh6Vuf8s//A77UXBOqk6YhzgIuooQhzrnv7aKI2L3I8w4Q7zXdNm1Sno+w7+/veeA+Un/cVSXEOhU4LCKezNtrkZaZqG1/5khw4ukykhYDfQMLRJoi5Rk87LNj5VmUPxglrBLbItYlwH5NH5y/ioh/Kzt2WSTtT5oX7p9lzwvXatSfRwIOnZvaukxEjEh7fZ6iZ5e8OSMiLhiJ6yhKi/4XSP0vM4EfF1zzWQTMzUmh70tDKTerAi/rSzo5xhOSXl5CnCr9T0T8umFeuG+R5oXbroRYoyStFRFPwAvNfP4cHSL/wmzYJB1HWgOlb6j2YXmI7pdG8LKGaz6wNksvp/wo8HrS9P4fLDDW7/OjCr2S1o+I++GFWczr3uxR5bxw3yKtdXU26fe2P/C1kmJ1LTe12bBJmgNMjIjevD0auKXoVS2rJOmKiNilVZmkeVHgsspVkrQHMA3oG/W1CzA1Ii4auasaHkkXAA+R5oXbmnTj9A0RsWVJ8SaQhtoLuDRKXka8G7nGY0VZk7TKJKRZEupu7aaawfqkmYkhLZlQmDxq7hvABNJ9KABExIZFxsnn/KOkN5ImkBVpyegqZuAu0/6keeFOjIgn87xwXygx3kuApyPiZ5LWlrRBRNxbYryu48RjRfgGcEu+g1ykb9F1bmYD+E/gKkl/Ib2nDYBP5qmITi041s+ALwMnAbsBH8oxC5en44E0jBpgQr5n6Ioy4lWhynnhJH2ZNFx7Y9K/28rAL4Ady4jXrdzUZoXI3zK3IX1gXl/WCpBVylP5b0J6T3eUNVu0pJsiYuvGmREkXRkRO5cQ6/yGzTGkBehuKml6ma4jaRawFXBzw4wWc+rcrDwSXOOxYZP0HuDPEXFe3l5T0rsjoqrZncuyEemb7Rhgi1wz+HkJcRZJGgXcLekQUn9FKSPNIqLxDnwkvZq0kJ+1518REZICXpiM14bINR4bNkmzImJiU1mt723ITSqTSf0uFwJvB66KiMLv8s9LINxO6ic7ljSNzQkRcX3RsVrEFjBnJGbCriNJnyd9IXkrqYn5YOCMiPjuiF5YzbjGY0VotbJk3f+29gW2JI3O+5CkdYCTS4oVpDn2XkPqM4A0ZLvw5htJ32XJ8OlRwERgdtFxutjawNmkPrKNgaNIo+lsCFzjsWGTdArwJPB90ofap4G1ImLKSF7XcPSt1ppnqd6NtJzyrWUMo5Z0J2kU1lzSMhYAlDG1jaSDGjZ7gPual0mw/vUzu7f7eIao7t9KrTN8GjgSODNvXwz8z8hdTiFmSlqTVPO4CXiKtJxyGf7e1z9WtogoekTeCkHSJ4BPAhvm+9b6rA44cQ+Razxmg5A0HlgjIuYMsuvynv8twIHApZS0xoukuQwwQ4G/sQ9M0jhgLVK/zuENLy2MiMdbH2X9ceKxYevGiScBJL2S1O/yQstAGfe7SPoFadj2PJY0tUWRMx7nqXEgLQoIqU8J0rLoz0TEMUXFMhuME48NWzfO2CvpeNL8bLexZC6wKGPp6zJXNm0R6+qI2HGwMrMyuY/HitA88eR46j/x5LuBjSPiuUH3HL7rJE2oaM6v1STt1LdWjaQ3Ab4XxSrlxGNFOII0vcxSE0+O4PUUYT5paHMViWcn4KC8oNlzLFk7qYx+lw8Dp+Q+C0ijEb2ImVXKTW1WiLymy1RgFulO/7/Vef4vSb8h3cfT3OFf+Bo5Df0vSylzpVBJa5D+/y998TmzZq7x2LBJ+ghwGPAqUuLZHriWNHV8XZ2XH6WrcinqXNP5MnnRvlxLPcYJyKrkGo8NWx6quw1wXURMlLQJ8JWIOGCEL82a5JrcrSyZYfuDwJYR8d6Ruypb0bjGY0VYFBGLJCHpRRFxh6SNR/qihqPKNXIq9tqI2Kdh+yt5xmWzyrSaY8tsqB7Md/mfA1wi6Vzg4RG+puH6GfBD0rQyuwE/Z8m9L3X2rKSd+jYk7UhasdOsMm5qs0JJ2pW0AukfI6LQlTqrVOUaOVWStCUpifaNansCOKisWRnMWnFTmxUqIi4ffK9aqGyNnKrk97NxRGyZR7UREf8c5DCzwrnGY9bCSK6RUyZJV0TELoPvaVYeJx6zFiRNIt0Y27hGTlk3dVZG0pGkPp0zgaf7yj3RpVXJiceshSrXyKlSnh1hmf/pu2C0ntWI+3jMWqtsjZyKTSCtK7MTKQFdCfxoRK/IVjiu8Zi1UMUaOSNB0q9JyzafnosOBNaMiP1H7qpsReMaj1lrHyKtkbMyDWvkALVOPORRbQ3bl0maPWJXYyskJx6z1rasao2cit0iafuIuA5A0nZ46WarmBOPWWtVrpFTpe2A/5B0f95eH7i9b2nsuo/as3pwH49ZC5JuB14LVLFGTmX6W4KhT91H7Vk9OPGYtTASa+SYrSiceMzMrFKendrMzCrlxGNmZpVy4jEzs0o58ZiZWaWceMzMrFL/H1WCds1rfCv0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#实例：查看各个部门离职率之间是否有明显差异\n",
    "#使用独立t检验方法，两两间求t检验统计量并求出p值,得到各个部门离职的分布\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.stats as ss\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "df = pd.read_csv(\"HR.csv\")\n",
    "dp_indices = df.groupby(by=\"sales\").indices    #按部门分组，用indices得到索引\n",
    "# print(dp_indices)\n",
    "sales_values = df[\"left\"].iloc[dp_indices[\"sales\"]].values\n",
    "#取出“left”中sales中的值\n",
    "technical_values = df[\"left\"].iloc[dp_indices[\"technical\"]].values\n",
    "\n",
    "print(ss.ttest_ind(sales_values,technical_values))  #打印p统计量\n",
    "#两两求p值,热力图\n",
    "dp_keys = list(dp_indices.keys()) #取出indices的keys//python3\n",
    "print(dp_keys)\n",
    "dp_t_mat = np.zeros([len(dp_keys),len(dp_keys)])    #初始化矩阵\n",
    "for i in range(len(dp_keys)):\n",
    "   for j in range(len(dp_keys)):\n",
    "       p_value = ss.ttest_ind(df[\"left\"].iloc[dp_indices[dp_keys[i]]].values,\\\n",
    "                              df[\"left\"].iloc[dp_indices[dp_keys[j]]].values)[1]\n",
    "       if p_value<0.05:\n",
    "           dp_t_mat[i][j]=-1\n",
    "       else:\n",
    "           dp_t_mat[i][j] = p_value\n",
    "    \n",
    "print(dp_t_mat)\n",
    "sns.heatmap(dp_t_mat,xticklabels=dp_keys,yticklabels=dp_keys)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Work_accident                        0         1\n",
      "promotion_last_5years salary                    \n",
      "0                     high    0.082996  0.000000\n",
      "                      low     0.331728  0.090020\n",
      "                      medium  0.230683  0.081655\n",
      "1                     high    0.000000  0.000000\n",
      "                      low     0.229167  0.166667\n",
      "                      medium  0.028986  0.023256\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#透视表\n",
    "piv_tb = pd.pivot_table(df,values=\"left\",index=[\"promotion_last_5years\",\"salary\"],columns=[\"Work_accident\"],aggfunc=np.mean)\n",
    "print(piv_tb)\n",
    "sns.heatmap(piv_tb,vmin=0,vmax=1,cmap=sns.color_palette(\"Reds\"))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#实例：分组分析\n",
    "import pandas as pd\n",
    "import scipy.stats as ss\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_context(font_scale=1.5)\n",
    "df = pd.read_csv(\"HR.csv\")\n",
    "#离散值：\n",
    "#设置hue向下根据部门钻取\n",
    "sns.barplot(x=\"salary\",y=\"left\",hue=\"sales\",data=df)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#连续值\n",
    "sl_s = df[\"satisfaction_level\"]\n",
    "sns.barplot(list(range(len(sl_s))),sl_s.sort_values())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#实例：相关分析\n",
    "import pandas as pd\n",
    "import scipy.stats as ss\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import math\n",
    "\n",
    "sns.set_context(font_scale=1.5)\n",
    "df = pd.read_csv(\"HR.csv\")\n",
    "\n",
    "#计算相关技术，画相关图（会自动去除离散属性）\n",
    "sns.heatmap(df.corr(),vmax=1,vmin=-1,cmap=sns.color_palette(\"RdBu\",n_colors=128))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9182958340544896\n",
      "1.0\n",
      "0.5408520829727552\n",
      "0.4591479170272448\n",
      "0.4591479170272448\n",
      "0.4591479170272448\n",
      "0.4591479170272448\n",
      "0.5\n",
      "0.4791387674918639\n",
      "0.4791387674918639\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anacondainstall\\lib\\site-packages\\ipykernel_launcher.py:20: FutureWarning: pd.groupby() is deprecated and will be removed; Please use the Series.groupby() or DataFrame.groupby() methods\n"
     ]
    }
   ],
   "source": [
    "#离散情况下：\n",
    "import numpy as np\n",
    "#Gini\n",
    "def getGini(a1, a2):\n",
    "    assert (len(a1) == len(a2))\n",
    "    d = dict()\n",
    "    for i in list(range(len(a1))):\n",
    "        d[a1[i]] = d.get(a1[i], []) + [a2[i]]\n",
    "    return 1 - sum([getProbSS(d[k]) * len(d[k]) / float(len(a1)) for k in d])\n",
    "#可能性平方和\n",
    "def getProbSS(s):\n",
    "    if not isinstance(s,pd.core.series.Series):\n",
    "        s = pd.Series(s)\n",
    "    prt_ary = np.array(pd.groupby(s, by=s).count().values / float(len(s)))\n",
    "    return sum(prt_ary ** 2)\n",
    "#熵\n",
    "def getEntropy(s):\n",
    "    if not isinstance(s, pd.core.series.Series):\n",
    "        s = pd.Series(s)\n",
    "        #计算其的分布\n",
    "    prt_ary = np.array(pd.groupby(s, by=s).count().values / float(len(s)))#（得到概率分布）\n",
    "    return -(np.log2(prt_ary) * prt_ary).sum()\n",
    "#条件熵\n",
    "def getCondEntropy(a1, a2):\n",
    "    assert (len(a1) == len(a2))\n",
    "    d = dict()\n",
    "    for i in list(range(len(a1))):\n",
    "        d[a1[i]] = d.get(a1[i], []) + [a2[i]]\n",
    "    return sum([getEntropy(d[k]) * len(d[k]) / float(len(a1)) for k in d])\n",
    "#熵增益\n",
    "def getEntropyGain(a1, a2):\n",
    "    return getEntropy(a2) - getCondEntropy(a1, a2)\n",
    "#熵增益率\n",
    "def getEntropyGainRatio(a1, a2):\n",
    "    return getEntropyGain(a1, a2) / getEntropy(a2)\n",
    "#相关度\n",
    "def getDiscreteRelation(a1, a2):\n",
    "    return getEntropyGain(a1, a2) / math.sqrt(getEntropy(a1) * getEntropy(a2))\n",
    "\n",
    "\n",
    "#离散相关性度量\n",
    "s1 = pd.Series([\"X1\", \"X1\", \"X2\", \"X2\", \"X2\", \"X2\"])\n",
    "s2 = pd.Series([\"Y1\", \"Y1\", \"Y1\", \"Y2\", \"Y2\", \"Y2\"])\n",
    "print(getEntropy(s1))\n",
    "print(getEntropy(s2))\n",
    "\n",
    "\n",
    "print(getCondEntropy(s1, s2))\n",
    "print(getCondEntropy(s2, s1))\n",
    "print(getEntropyGain(s1, s2))\n",
    "print(getEntropyGain(s2, s1))\n",
    "print(getEntropyGainRatio(s1, s2))\n",
    "print(getEntropyGainRatio(s2, s1))\n",
    "print(getDiscreteRelation(s1, s2))\n",
    "print(getDiscreteRelation(s2, s1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ratio: [9.98565340e-01 8.69246970e-04 4.73865973e-04 4.96932182e-05\n",
      " 2.43172315e-05 9.29496619e-06 8.24128218e-06]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set_context(font_scale=1.5)\n",
    "df=pd.read_csv(\"HR.csv\")\n",
    "from sklearn.decomposition import PCA\n",
    "my_pca=PCA(n_components=7)\n",
    "my_pca.fit_transform(df.drop(labels=[\"salary\",\"sales\",\"left\"],axis=1))\n",
    "print(\"Ratio:\",my_pca.explained_variance_ratio_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据建模与挖掘 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据清洗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#特征中异常值（空值）的处理和转换，数据清洗\n",
    "#异常的查询和识别\n",
    "#pandas：isnull（）/duplicated（）\n",
    "#异常值（空值的丢弃）\n",
    "#pandas ：drop()/dropna()/drop_duplicated()\n",
    "#异常值的处理，用中值或者其他值替代原值，边界值的取代\n",
    "# pandas：fillna（）\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "df=pd.DataFrame({\"A\":[\"a0\",\"a1\",\"a1\",\"a2\",\"a3\",\"a4\"],\"B\":[\"b0\",\"b1\",\"b2\",\"b2\",\"b3\",None],\n",
    "                 \"C\":[1,2,None,3,4,5],\"D\":[0.1,10.2,11.4,8.9,9.1,12],\n",
    "                 \"E\":[10,19,32,25,8,None],\"F\":[\"f0\",\"g1\",\"f2\",\"f3\",\"f4\",\"f5\"]})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a0</td>\n",
       "      <td>b0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a1</td>\n",
       "      <td>b1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.2</td>\n",
       "      <td>19.0</td>\n",
       "      <td>g1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a1</td>\n",
       "      <td>b2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.4</td>\n",
       "      <td>32.0</td>\n",
       "      <td>f2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a2</td>\n",
       "      <td>b2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>f3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a3</td>\n",
       "      <td>b3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>f4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>a4</td>\n",
       "      <td>None</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A     B    C     D     E   F\n",
       "0  a0    b0  1.0   0.1  10.0  f0\n",
       "1  a1    b1  2.0  10.2  19.0  g1\n",
       "2  a1    b2  NaN  11.4  32.0  f2\n",
       "3  a2    b2  3.0   8.9  25.0  f3\n",
       "4  a3    b3  4.0   9.1   8.0  f4\n",
       "5  a4  None  5.0  12.0   NaN  f5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       A      B      C      D      E      F\n",
       "0  False  False  False  False  False  False\n",
       "1  False  False  False  False  False  False\n",
       "2  False  False   True  False  False  False\n",
       "3  False  False  False  False  False  False\n",
       "4  False  False  False  False  False  False\n",
       "5  False   True  False  False   True  False"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#识别空值\n",
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>f4</td>\n",
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      "text/plain": [
       "    A   B    C     D     E   F\n",
       "0  a0  b0  1.0   0.1  10.0  f0\n",
       "1  a1  b1  2.0  10.2  19.0  g1\n",
       "3  a2  b2  3.0   8.9  25.0  f3\n",
       "4  a3  b3  4.0   9.1   8.0  f4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna()#删除空值所在行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>f4</td>\n",
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      ],
      "text/plain": [
       "    A   B    C     D     E   F\n",
       "0  a0  b0  1.0   0.1  10.0  f0\n",
       "1  a1  b1  2.0  10.2  19.0  g1\n",
       "2  a1  b2  NaN  11.4  32.0  f2\n",
       "3  a2  b2  3.0   8.9  25.0  f3\n",
       "4  a3  b3  4.0   9.1   8.0  f4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(subset=[\"B\"])#删除B属性的空值的行，保留C属性的空值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "4    False\n",
       "5    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated([\"A\"])#查找A属性的重复值，"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated([\"A\",\"B\"])#联合A/B属性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <th>5</th>\n",
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       "</div>"
      ],
      "text/plain": [
       "    A     B    C     D     E   F\n",
       "0  a0    b0  1.0   0.1  10.0  f0\n",
       "1  a1    b1  2.0  10.2  19.0  g1\n",
       "3  a2    b2  3.0   8.9  25.0  f3\n",
       "4  a3    b3  4.0   9.1   8.0  f4\n",
       "5  a4  None  5.0  12.0   NaN  f5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates([\"A\"])#删除A属性重复的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "    A     B    C     D     E   F\n",
       "0  a0    b0  1.0   0.1  10.0  f0\n",
       "3  a2    b2  3.0   8.9  25.0  f3\n",
       "4  a3    b3  4.0   9.1   8.0  f4\n",
       "5  a4  None  5.0  12.0   NaN  f5"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates([\"A\"],keep=False)#删除A属性重复的行，keep保留哪个重复行（False‘first’，'last’）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a3</td>\n",
       "      <td>b3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>f4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>a4</td>\n",
       "      <td>None</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A     B    C     D     E   F\n",
       "0  a0    b0  1.0   0.1  10.0  f0\n",
       "1  a1    b1  2.0  10.2  19.0  g1\n",
       "2  a1    b2  NaN  11.4  32.0  f2\n",
       "3  a2    b2  3.0   8.9  25.0  f3\n",
       "4  a3    b3  4.0   9.1   8.0  f4\n",
       "5  a4  None  5.0  12.0   NaN  f5"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>8.9</td>\n",
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       "      <th>5</th>\n",
       "      <td>a4</td>\n",
       "      <td>b*</td>\n",
       "      <td>5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>b*</td>\n",
       "      <td>f5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B   C     D   E   F\n",
       "0  a0  b0   1   0.1  10  f0\n",
       "1  a1  b1   2  10.2  19  g1\n",
       "2  a1  b2  b*  11.4  32  f2\n",
       "3  a2  b2   3   8.9  25  f3\n",
       "4  a3  b3   4   9.1   8  f4\n",
       "5  a4  b*   5  12.0  b*  f5"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(\"b*\")#空值赋值为b*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "      <th>5</th>\n",
       "      <td>a4</td>\n",
       "      <td>18.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.8</td>\n",
       "      <td>f5</td>\n",
       "    </tr>\n",
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       "</div>"
      ],
      "text/plain": [
       "    A     B     C     D     E   F\n",
       "0  a0    b0   1.0   0.1  10.0  f0\n",
       "1  a1    b1   2.0  10.2  19.0  g1\n",
       "2  a1    b2  18.8  11.4  32.0  f2\n",
       "3  a2    b2   3.0   8.9  25.0  f3\n",
       "4  a3    b3   4.0   9.1   8.0  f4\n",
       "5  a4  18.8   5.0  12.0  18.8  f5"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(df[\"E\"].mean())#空值赋值为E属性的均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "      <td>b3</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>a4</td>\n",
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       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "    A     B    C     D     E   F\n",
       "0  a0    b0  1.0   0.1  10.0  f0\n",
       "1  a1    b1  2.0  10.2  19.0  g1\n",
       "2  a1    b2  NaN  11.4  32.0  f2\n",
       "3  a2    b2  3.0   8.9  25.0  f3\n",
       "4  a3    b3  4.0   9.1   8.0  f4\n",
       "5  a4  None  5.0  12.0   NaN  f5"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10.0\n",
       "1    19.0\n",
       "2    32.0\n",
       "3    25.0\n",
       "4     8.0\n",
       "5     8.0\n",
       "Name: E, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"E\"].interpolate()#插值，只针对Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1.0\n",
       "1     2.5\n",
       "2     4.0\n",
       "3     5.0\n",
       "4    20.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([1,None,4,5,20]).interpolate()#复制为附近两值的平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1.00\n",
       "1     6.25\n",
       "2     4.00\n",
       "3     5.00\n",
       "4    20.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([1,None,4,5,20]).interpolate(method=\"spline\",order=3)#三次条样差值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "    A     B    C     D     E   F\n",
       "0  a0    b0  1.0   0.1  10.0  f0\n",
       "1  a1    b1  2.0  10.2  19.0  g1\n",
       "2  a1    b2  NaN  11.4  32.0  f2\n",
       "3  a2    b2  3.0   8.9  25.0  f3\n",
       "4  a3    b3  4.0   9.1   8.0  f4\n",
       "5  a4  None  5.0  12.0   NaN  f5"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#D0可能是异常值，四分维书来决定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "upper=df[\"D\"].quantile(0.75)#上分位数\n",
    "lower=df[\"D\"].quantile(0.25)#下分维数\n",
    "q_int=upper-lower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "k=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anacondainstall\\lib\\site-packages\\ipykernel_launcher.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
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       "      <th>4</th>\n",
       "      <td>a3</td>\n",
       "      <td>b3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>f4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>a4</td>\n",
       "      <td>None</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A     B    C     D     E   F\n",
       "1  a1    b1  2.0  10.2  19.0  g1\n",
       "2  a1    b2  NaN  11.4  32.0  f2\n",
       "3  a2    b2  3.0   8.9  25.0  f3\n",
       "4  a3    b3  4.0   9.1   8.0  f4\n",
       "5  a4  None  5.0  12.0   NaN  f5"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"D\"]>lower-k*q_int][df[\"D\"]<upper+k*q_int]#第一行被删除了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a0</td>\n",
       "      <td>b0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a1</td>\n",
       "      <td>b1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.2</td>\n",
       "      <td>19.0</td>\n",
       "      <td>g1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a1</td>\n",
       "      <td>b2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.4</td>\n",
       "      <td>32.0</td>\n",
       "      <td>f2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a2</td>\n",
       "      <td>b2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>f3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a3</td>\n",
       "      <td>b3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>f4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>a4</td>\n",
       "      <td>None</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A     B    C     D     E   F\n",
       "0  a0    b0  1.0   0.1  10.0  f0\n",
       "1  a1    b1  2.0  10.2  19.0  g1\n",
       "2  a1    b2  NaN  11.4  32.0  f2\n",
       "3  a2    b2  3.0   8.9  25.0  f3\n",
       "4  a3    b3  4.0   9.1   8.0  f4\n",
       "5  a4  None  5.0  12.0   NaN  f5"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除F1\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a0</td>\n",
       "      <td>b0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a1</td>\n",
       "      <td>b2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.4</td>\n",
       "      <td>32.0</td>\n",
       "      <td>f2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a2</td>\n",
       "      <td>b2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>25.0</td>\n",
       "      <td>f3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a3</td>\n",
       "      <td>b3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>f4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>a4</td>\n",
       "      <td>None</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A     B    C     D     E   F\n",
       "0  a0    b0  1.0   0.1  10.0  f0\n",
       "2  a1    b2  NaN  11.4  32.0  f2\n",
       "3  a2    b2  3.0   8.9  25.0  f3\n",
       "4  a3    b3  4.0   9.1   8.0  f4\n",
       "5  a4  None  5.0  12.0   NaN  f5"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#判断df[“F”]中的值，是否开头为“f‘\n",
    "df[[True if item.startswith(\"f\") else False for item in list(df[\"F\"].values)]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "####特征预处理，标注\n",
    "# 特征选择\n",
    "# 特征变换\n",
    "# 对指化，离散化，数据平滑，归一化（标准化）、\n",
    "# 数值降维\n",
    "# 特征衍生"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 特征选择 剔除与标注不相关或者冗余的特征\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as ss\n",
    "df=pd.DataFrame({\"A\":ss.norm.rvs(size=10),\"B\":ss.norm.rvs(size=10),\n",
    "\"C\":ss.norm.rvs(size=10),\"D\":np.random.randint(low=0,high=2,size=10)})#随机选择（D为label）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.589685</td>\n",
       "      <td>0.605356</td>\n",
       "      <td>0.933947</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.156238</td>\n",
       "      <td>2.058218</td>\n",
       "      <td>-0.430355</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.762866</td>\n",
       "      <td>-0.222717</td>\n",
       "      <td>1.311641</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.798272</td>\n",
       "      <td>-0.078432</td>\n",
       "      <td>1.127526</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.220521</td>\n",
       "      <td>-0.968824</td>\n",
       "      <td>1.832401</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.950548</td>\n",
       "      <td>-0.898886</td>\n",
       "      <td>1.350662</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.000313</td>\n",
       "      <td>0.111857</td>\n",
       "      <td>0.530304</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.339627</td>\n",
       "      <td>0.060072</td>\n",
       "      <td>-0.420410</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.032094</td>\n",
       "      <td>-0.885717</td>\n",
       "      <td>-1.269371</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.681239</td>\n",
       "      <td>-0.682203</td>\n",
       "      <td>1.147339</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C  D\n",
       "0 -0.589685  0.605356  0.933947  0\n",
       "1  2.156238  2.058218 -0.430355  1\n",
       "2  0.762866 -0.222717  1.311641  0\n",
       "3 -1.798272 -0.078432  1.127526  1\n",
       "4  1.220521 -0.968824  1.832401  1\n",
       "5 -0.950548 -0.898886  1.350662  0\n",
       "6 -1.000313  0.111857  0.530304  1\n",
       "7 -0.339627  0.060072 -0.420410  1\n",
       "8  0.032094 -0.885717 -1.269371  1\n",
       "9  1.681239 -0.682203  1.147339  0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.loc[:,[\"A\",\"B\",\"C\"]]\n",
    "Y=df.loc[:,\"D\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#过滤思想，包裹，嵌入思想\n",
    "from sklearn.feature_selection import SelectKBest,RFE,SelectFromModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "skb=SelectKBest(k=2)#k=2保留两个特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SelectKBest(k=2, score_func=<function f_classif at 0x000002652986A6A8>)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skb.fit(X,Y)#拟合一下\n",
    "#说明sorce_func可以调用，可以查看sklearn官网的API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.60535573,  0.93394694],\n",
       "       [ 2.05821807, -0.43035498],\n",
       "       [-0.22271673,  1.31164118],\n",
       "       [-0.07843217,  1.12752572],\n",
       "       [-0.9688237 ,  1.83240109],\n",
       "       [-0.898886  ,  1.35066193],\n",
       "       [ 0.11185696,  0.53030373],\n",
       "       [ 0.06007181, -0.42040994],\n",
       "       [-0.88571669, -1.26937104],\n",
       "       [-0.68220346,  1.14733946]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skb.transform(X)#A，C与D的相关性较好，所以B被干掉了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfe=RFE(estimator=SVR(kernel=\"linear\"),n_features_to_select=2,step=1)#step为每次迭代，去除一个特征\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.58968476,  0.93394694],\n",
       "       [ 2.15623819, -0.43035498],\n",
       "       [ 0.76286584,  1.31164118],\n",
       "       [-1.79827242,  1.12752572],\n",
       "       [ 1.22052077,  1.83240109],\n",
       "       [-0.95054753,  1.35066193],\n",
       "       [-1.00031349,  0.53030373],\n",
       "       [-0.33962666, -0.42040994],\n",
       "       [ 0.03209351, -1.26937104],\n",
       "       [ 1.68123929,  1.14733946]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfe.fit_transform(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "sfm=SelectFromModel(estimator=DecisionTreeRegressor(),threshold=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.93394694],\n",
       "       [-0.43035498],\n",
       "       [ 1.31164118],\n",
       "       [ 1.12752572],\n",
       "       [ 1.83240109],\n",
       "       [ 1.35066193],\n",
       "       [ 0.53030373],\n",
       "       [-0.42040994],\n",
       "       [-1.26937104],\n",
       "       [ 1.14733946]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "sfm.fit_transform(X,Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征变换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#特征变换\n",
    "# ---对指化，大于0的时候，数据会变大（概率--softmax）numpy.exp\n",
    "# ---收入的数据，声音的强度（对数化，缩放在较小的数据内）numpy.log\n",
    "# ---离散化，将连续变量分成几段（bins）\n",
    "# 克服数据缺陷，连续的变量有噪声的影响等频\n",
    "# 非线性的映射，自因变量优化\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "#分箱技术，（离散化）\n",
    "lst=[6,8,10,15,16,24,25,40,67]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[low, low, low, medium, medium, medium, high, high, high]\n",
       "Categories (3, object): [low < medium < high]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#等深分箱\n",
    "pd.qcut(lst,q=3)\n",
    "pd.qcut(lst,q=3,labels=[\"low\",\"medium\",\"high\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[low, low, low, low, low, low, low, medium, high]\n",
       "Categories (3, object): [low < medium < high]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#等宽分箱,即最大值减去最小值的差进行平分\n",
    "pd.cut(lst,bins=3,labels=[\"low\",\"medium\",\"high\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 归一化   缩放带0~1之间\n",
    "#标准化 z_score：\n",
    "# 1 1 1 1  0 0 0 0 \n",
    "# 1 1 1 1 -1 -1 -1 -1 \n",
    "\n",
    "# 1 0 0 0 0 0 0 0 \n",
    "# 2.64 -0.38 -0.38 -0.38 -0.38 -0.38 -0.38 -0.38 \n",
    "#加强数据内重要的特征的对比差度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler,StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anacondainstall\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int32 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.  ],\n",
       "       [0.15],\n",
       "       [0.45],\n",
       "       [0.7 ],\n",
       "       [1.  ]])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MinMaxScaler().fit_transform(np.array([1,4,10,15,21]).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anacondainstall\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int32 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 1.],\n",
       "       [ 1.],\n",
       "       [ 1.],\n",
       "       [ 1.],\n",
       "       [-1.],\n",
       "       [-1.],\n",
       "       [-1.],\n",
       "       [-1.]])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "StandardScaler().fit_transform(np.array([1,1,1,1,0,0,0,0 ]).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anacondainstall\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int32 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 2.64575131],\n",
       "       [-0.37796447],\n",
       "       [-0.37796447],\n",
       "       [-0.37796447],\n",
       "       [-0.37796447],\n",
       "       [-0.37796447],\n",
       "       [-0.37796447],\n",
       "       [-0.37796447]])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "StandardScaler().fit_transform(np.array([1,0,0,0,0,0,0,0 ]).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数值化\n",
    "# （定类数据，定序，定距，定比）\n",
    "定序：数值化，（高-1，中，0，低，-1）\n",
    "定类数据：One—Hot—encode\n",
    "定距：归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder,OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anacondainstall\\lib\\site-packages\\sklearn\\preprocessing\\label.py:111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 2], dtype=int64)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LabelEncoder().fit_transform(np.array([\"Low\",\"Medium\",\"Low\",\"high\"]).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "b_encoder=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "b_tran_f=b_encoder.fit_transform(np.array([\"Red\",\"yellow\",\"blue\",\"Greeen\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "oht_encoder=OneHotEncoder().fit(b_tran_f.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<5x4 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 5 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#得到一个稀疏的矩阵\n",
    "oht_encoder.transform(b_encoder.transform(np.array([\"yellow\",\"blue\",\"Red\",\"Greeen\",\"Greeen\"])).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 1.],\n",
       "       [0., 0., 1., 0.],\n",
       "       [0., 1., 0., 0.],\n",
       "       [1., 0., 0., 0.],\n",
       "       [1., 0., 0., 0.]])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oht_encoder.transform(b_encoder.transform(np.array([\"yellow\",\"blue\",\"Red\",\"Greeen\",\"Greeen\"])).reshape(-1,1)).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "#正规化\n",
    "from sklearn.preprocessing import Normalizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.],\n",
       "       [ 1.],\n",
       "       [ 1.],\n",
       "       [-1.],\n",
       "       [ 1.]])"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Normalizer().fit_transform(np.array([1,1,3,-1,2]).reshape(-1,1))#对行的正规化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.25,  0.25,  0.75, -0.25,  0.5 ]])"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Normalizer(norm=\"l2\").fit_transform(np.array([[1,1,3,-1,2]]))#对行的正规化\"l1或者l1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "#特征降维\n",
    "#LDA---线性判别分析\n",
    "# 核心思想：投影变换后同一标注内距离竟可能小，不同标注间距离竟可能大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=np.array([[-1,-1],[-2,-1],[-3,-2],[1,1],[2,1],[3,2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y=np.array([1,1,1,2,2,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.73205081],\n",
       "       [-1.73205081],\n",
       "       [-3.46410162],\n",
       "       [ 1.73205081],\n",
       "       [ 1.73205081],\n",
       "       [ 3.46410162]])"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LinearDiscriminantAnalysis(n_components=1).fit_transform(X,Y)#降维降成一维"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf=LinearDiscriminantAnalysis(n_components=1).fit(X,Y)#fisher 分类器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2])"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict([[0.8,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "#特征衍生（发现的特征，不是很大）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HR表的预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>sales</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.41</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.77</td>\n",
       "      <td>6</td>\n",
       "      <td>247</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.92</td>\n",
       "      <td>0.85</td>\n",
       "      <td>5</td>\n",
       "      <td>259</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.89</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5</td>\n",
       "      <td>224</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.42</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>142</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0                0.38             0.53               2                   157   \n",
       "1                0.80             0.86               5                   262   \n",
       "2                0.11             0.88               7                   272   \n",
       "3                0.72             0.87               5                   223   \n",
       "4                0.37             0.52               2                   159   \n",
       "5                0.41             0.50               2                   153   \n",
       "6                0.10             0.77               6                   247   \n",
       "7                0.92             0.85               5                   259   \n",
       "8                0.89             1.00               5                   224   \n",
       "9                0.42             0.53               2                   142   \n",
       "\n",
       "   time_spend_company  Work_accident  left  promotion_last_5years  sales  \\\n",
       "0                   3              0     1                      0  sales   \n",
       "1                   6              0     1                      0  sales   \n",
       "2                   4              0     1                      0  sales   \n",
       "3                   5              0     1                      0  sales   \n",
       "4                   3              0     1                      0  sales   \n",
       "5                   3              0     1                      0  sales   \n",
       "6                   4              0     1                      0  sales   \n",
       "7                   5              0     1                      0  sales   \n",
       "8                   5              0     1                      0  sales   \n",
       "9                   3              0     1                      0  sales   \n",
       "\n",
       "   salary  \n",
       "0     low  \n",
       "1  medium  \n",
       "2  medium  \n",
       "3     low  \n",
       "4     low  \n",
       "5     low  \n",
       "6     low  \n",
       "7     low  \n",
       "8     low  \n",
       "9     low  "
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import MinMaxScaler,StandardScaler\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.preprocessing import Normalizer\n",
    "\n",
    "df = pd.read_csv('HR.csv')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1得到标注---left\n",
    "label=df[\"left\"]\n",
    "df = df.drop('left', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>sales</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.41</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.77</td>\n",
       "      <td>6</td>\n",
       "      <td>247</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.92</td>\n",
       "      <td>0.85</td>\n",
       "      <td>5</td>\n",
       "      <td>259</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.89</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5</td>\n",
       "      <td>224</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.42</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>142</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.45</td>\n",
       "      <td>0.54</td>\n",
       "      <td>2</td>\n",
       "      <td>135</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.81</td>\n",
       "      <td>6</td>\n",
       "      <td>305</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.84</td>\n",
       "      <td>0.92</td>\n",
       "      <td>4</td>\n",
       "      <td>234</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.41</td>\n",
       "      <td>0.55</td>\n",
       "      <td>2</td>\n",
       "      <td>148</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.36</td>\n",
       "      <td>0.56</td>\n",
       "      <td>2</td>\n",
       "      <td>137</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.54</td>\n",
       "      <td>2</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.45</td>\n",
       "      <td>0.47</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.78</td>\n",
       "      <td>0.99</td>\n",
       "      <td>4</td>\n",
       "      <td>255</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.45</td>\n",
       "      <td>0.51</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.89</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.83</td>\n",
       "      <td>6</td>\n",
       "      <td>282</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.55</td>\n",
       "      <td>2</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.09</td>\n",
       "      <td>0.95</td>\n",
       "      <td>6</td>\n",
       "      <td>304</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.46</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2</td>\n",
       "      <td>139</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.89</td>\n",
       "      <td>0.92</td>\n",
       "      <td>5</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.82</td>\n",
       "      <td>0.87</td>\n",
       "      <td>4</td>\n",
       "      <td>239</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.49</td>\n",
       "      <td>2</td>\n",
       "      <td>135</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.41</td>\n",
       "      <td>0.46</td>\n",
       "      <td>2</td>\n",
       "      <td>128</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>accounting</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2</td>\n",
       "      <td>132</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>accounting</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14969</th>\n",
       "      <td>0.43</td>\n",
       "      <td>0.46</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14970</th>\n",
       "      <td>0.78</td>\n",
       "      <td>0.93</td>\n",
       "      <td>4</td>\n",
       "      <td>225</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14971</th>\n",
       "      <td>0.39</td>\n",
       "      <td>0.45</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14972</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.97</td>\n",
       "      <td>6</td>\n",
       "      <td>310</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>accounting</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14973</th>\n",
       "      <td>0.36</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>accounting</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14974</th>\n",
       "      <td>0.36</td>\n",
       "      <td>0.54</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>accounting</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14975</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.79</td>\n",
       "      <td>7</td>\n",
       "      <td>310</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hr</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14976</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.47</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hr</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14977</th>\n",
       "      <td>0.81</td>\n",
       "      <td>0.85</td>\n",
       "      <td>4</td>\n",
       "      <td>251</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hr</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14978</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.47</td>\n",
       "      <td>2</td>\n",
       "      <td>144</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hr</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14979</th>\n",
       "      <td>0.09</td>\n",
       "      <td>0.93</td>\n",
       "      <td>6</td>\n",
       "      <td>296</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14980</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.89</td>\n",
       "      <td>5</td>\n",
       "      <td>238</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14981</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.93</td>\n",
       "      <td>5</td>\n",
       "      <td>162</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14982</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.49</td>\n",
       "      <td>2</td>\n",
       "      <td>137</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14983</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.84</td>\n",
       "      <td>5</td>\n",
       "      <td>257</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14984</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.56</td>\n",
       "      <td>2</td>\n",
       "      <td>148</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14985</th>\n",
       "      <td>0.91</td>\n",
       "      <td>0.99</td>\n",
       "      <td>5</td>\n",
       "      <td>254</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14986</th>\n",
       "      <td>0.85</td>\n",
       "      <td>0.85</td>\n",
       "      <td>4</td>\n",
       "      <td>247</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14987</th>\n",
       "      <td>0.90</td>\n",
       "      <td>0.70</td>\n",
       "      <td>5</td>\n",
       "      <td>206</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14988</th>\n",
       "      <td>0.46</td>\n",
       "      <td>0.55</td>\n",
       "      <td>2</td>\n",
       "      <td>145</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14989</th>\n",
       "      <td>0.43</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14990</th>\n",
       "      <td>0.89</td>\n",
       "      <td>0.88</td>\n",
       "      <td>5</td>\n",
       "      <td>228</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14991</th>\n",
       "      <td>0.09</td>\n",
       "      <td>0.81</td>\n",
       "      <td>6</td>\n",
       "      <td>257</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14992</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2</td>\n",
       "      <td>155</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14993</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.83</td>\n",
       "      <td>6</td>\n",
       "      <td>293</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14994</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2</td>\n",
       "      <td>151</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.96</td>\n",
       "      <td>6</td>\n",
       "      <td>280</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "0                    0.38             0.53               2   \n",
       "1                    0.80             0.86               5   \n",
       "2                    0.11             0.88               7   \n",
       "3                    0.72             0.87               5   \n",
       "4                    0.37             0.52               2   \n",
       "5                    0.41             0.50               2   \n",
       "6                    0.10             0.77               6   \n",
       "7                    0.92             0.85               5   \n",
       "8                    0.89             1.00               5   \n",
       "9                    0.42             0.53               2   \n",
       "10                   0.45             0.54               2   \n",
       "11                   0.11             0.81               6   \n",
       "12                   0.84             0.92               4   \n",
       "13                   0.41             0.55               2   \n",
       "14                   0.36             0.56               2   \n",
       "15                   0.38             0.54               2   \n",
       "16                   0.45             0.47               2   \n",
       "17                   0.78             0.99               4   \n",
       "18                   0.45             0.51               2   \n",
       "19                   0.76             0.89               5   \n",
       "20                   0.11             0.83               6   \n",
       "21                   0.38             0.55               2   \n",
       "22                   0.09             0.95               6   \n",
       "23                   0.46             0.57               2   \n",
       "24                   0.40             0.53               2   \n",
       "25                   0.89             0.92               5   \n",
       "26                   0.82             0.87               4   \n",
       "27                   0.40             0.49               2   \n",
       "28                   0.41             0.46               2   \n",
       "29                   0.38             0.50               2   \n",
       "...                   ...              ...             ...   \n",
       "14969                0.43             0.46               2   \n",
       "14970                0.78             0.93               4   \n",
       "14971                0.39             0.45               2   \n",
       "14972                0.11             0.97               6   \n",
       "14973                0.36             0.52               2   \n",
       "14974                0.36             0.54               2   \n",
       "14975                0.10             0.79               7   \n",
       "14976                0.40             0.47               2   \n",
       "14977                0.81             0.85               4   \n",
       "14978                0.40             0.47               2   \n",
       "14979                0.09             0.93               6   \n",
       "14980                0.76             0.89               5   \n",
       "14981                0.73             0.93               5   \n",
       "14982                0.38             0.49               2   \n",
       "14983                0.72             0.84               5   \n",
       "14984                0.40             0.56               2   \n",
       "14985                0.91             0.99               5   \n",
       "14986                0.85             0.85               4   \n",
       "14987                0.90             0.70               5   \n",
       "14988                0.46             0.55               2   \n",
       "14989                0.43             0.57               2   \n",
       "14990                0.89             0.88               5   \n",
       "14991                0.09             0.81               6   \n",
       "14992                0.40             0.48               2   \n",
       "14993                0.76             0.83               6   \n",
       "14994                0.40             0.57               2   \n",
       "14995                0.37             0.48               2   \n",
       "14996                0.37             0.53               2   \n",
       "14997                0.11             0.96               6   \n",
       "14998                0.37             0.52               2   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident  \\\n",
       "0                       157                   3              0   \n",
       "1                       262                   6              0   \n",
       "2                       272                   4              0   \n",
       "3                       223                   5              0   \n",
       "4                       159                   3              0   \n",
       "5                       153                   3              0   \n",
       "6                       247                   4              0   \n",
       "7                       259                   5              0   \n",
       "8                       224                   5              0   \n",
       "9                       142                   3              0   \n",
       "10                      135                   3              0   \n",
       "11                      305                   4              0   \n",
       "12                      234                   5              0   \n",
       "13                      148                   3              0   \n",
       "14                      137                   3              0   \n",
       "15                      143                   3              0   \n",
       "16                      160                   3              0   \n",
       "17                      255                   6              0   \n",
       "18                      160                   3              1   \n",
       "19                      262                   5              0   \n",
       "20                      282                   4              0   \n",
       "21                      147                   3              0   \n",
       "22                      304                   4              0   \n",
       "23                      139                   3              0   \n",
       "24                      158                   3              0   \n",
       "25                      242                   5              0   \n",
       "26                      239                   5              0   \n",
       "27                      135                   3              0   \n",
       "28                      128                   3              0   \n",
       "29                      132                   3              0   \n",
       "...                     ...                 ...            ...   \n",
       "14969                   157                   3              0   \n",
       "14970                   225                   5              0   \n",
       "14971                   140                   3              0   \n",
       "14972                   310                   4              0   \n",
       "14973                   143                   3              0   \n",
       "14974                   153                   3              0   \n",
       "14975                   310                   4              0   \n",
       "14976                   136                   3              0   \n",
       "14977                   251                   6              0   \n",
       "14978                   144                   3              0   \n",
       "14979                   296                   4              0   \n",
       "14980                   238                   5              0   \n",
       "14981                   162                   4              0   \n",
       "14982                   137                   3              0   \n",
       "14983                   257                   5              0   \n",
       "14984                   148                   3              0   \n",
       "14985                   254                   5              0   \n",
       "14986                   247                   6              0   \n",
       "14987                   206                   4              0   \n",
       "14988                   145                   3              0   \n",
       "14989                   159                   3              1   \n",
       "14990                   228                   5              1   \n",
       "14991                   257                   4              0   \n",
       "14992                   155                   3              0   \n",
       "14993                   293                   6              0   \n",
       "14994                   151                   3              0   \n",
       "14995                   160                   3              0   \n",
       "14996                   143                   3              0   \n",
       "14997                   280                   4              0   \n",
       "14998                   158                   3              0   \n",
       "\n",
       "       promotion_last_5years       sales  salary  \n",
       "0                          0       sales     low  \n",
       "1                          0       sales  medium  \n",
       "2                          0       sales  medium  \n",
       "3                          0       sales     low  \n",
       "4                          0       sales     low  \n",
       "5                          0       sales     low  \n",
       "6                          0       sales     low  \n",
       "7                          0       sales     low  \n",
       "8                          0       sales     low  \n",
       "9                          0       sales     low  \n",
       "10                         0       sales     low  \n",
       "11                         0       sales     low  \n",
       "12                         0       sales     low  \n",
       "13                         0       sales     low  \n",
       "14                         0       sales     low  \n",
       "15                         0       sales     low  \n",
       "16                         0       sales     low  \n",
       "17                         0       sales     low  \n",
       "18                         1       sales     low  \n",
       "19                         0       sales     low  \n",
       "20                         0       sales     low  \n",
       "21                         0       sales     low  \n",
       "22                         0       sales     low  \n",
       "23                         0       sales     low  \n",
       "24                         0       sales     low  \n",
       "25                         0       sales     low  \n",
       "26                         0       sales     low  \n",
       "27                         0       sales     low  \n",
       "28                         0  accounting     low  \n",
       "29                         0  accounting     low  \n",
       "...                      ...         ...     ...  \n",
       "14969                      0       sales  medium  \n",
       "14970                      0       sales  medium  \n",
       "14971                      0       sales  medium  \n",
       "14972                      0  accounting  medium  \n",
       "14973                      0  accounting  medium  \n",
       "14974                      0  accounting  medium  \n",
       "14975                      0          hr  medium  \n",
       "14976                      0          hr  medium  \n",
       "14977                      0          hr  medium  \n",
       "14978                      0          hr  medium  \n",
       "14979                      0   technical  medium  \n",
       "14980                      0   technical    high  \n",
       "14981                      0   technical     low  \n",
       "14982                      0   technical  medium  \n",
       "14983                      0   technical  medium  \n",
       "14984                      0   technical  medium  \n",
       "14985                      0   technical  medium  \n",
       "14986                      0   technical     low  \n",
       "14987                      0   technical     low  \n",
       "14988                      0   technical     low  \n",
       "14989                      0   technical     low  \n",
       "14990                      0     support     low  \n",
       "14991                      0     support     low  \n",
       "14992                      0     support     low  \n",
       "14993                      0     support     low  \n",
       "14994                      0     support     low  \n",
       "14995                      0     support     low  \n",
       "14996                      0     support     low  \n",
       "14997                      0     support     low  \n",
       "14998                      0     support     low  \n",
       "\n",
       "[14999 rows x 9 columns]"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1        1\n",
       "2        1\n",
       "3        1\n",
       "4        1\n",
       "5        1\n",
       "6        1\n",
       "7        1\n",
       "8        1\n",
       "9        1\n",
       "10       1\n",
       "11       1\n",
       "12       1\n",
       "13       1\n",
       "14       1\n",
       "15       1\n",
       "16       1\n",
       "17       1\n",
       "18       1\n",
       "19       1\n",
       "20       1\n",
       "21       1\n",
       "22       1\n",
       "23       1\n",
       "24       1\n",
       "25       1\n",
       "26       1\n",
       "27       1\n",
       "28       1\n",
       "29       1\n",
       "        ..\n",
       "14969    1\n",
       "14970    1\n",
       "14971    1\n",
       "14972    1\n",
       "14973    1\n",
       "14974    1\n",
       "14975    1\n",
       "14976    1\n",
       "14977    1\n",
       "14978    1\n",
       "14979    1\n",
       "14980    1\n",
       "14981    1\n",
       "14982    1\n",
       "14983    1\n",
       "14984    1\n",
       "14985    1\n",
       "14986    1\n",
       "14987    1\n",
       "14988    1\n",
       "14989    1\n",
       "14990    1\n",
       "14991    1\n",
       "14992    1\n",
       "14993    1\n",
       "14994    1\n",
       "14995    1\n",
       "14996    1\n",
       "14997    1\n",
       "14998    1\n",
       "Name: left, Length: 14999, dtype: int64"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2清洗数据（数据较少，不抽样操作）\n",
    "df=df.dropna(subset=[\"satisfaction_level\",\"last_evaluation\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df[df[\"satisfaction_level\"]<=1][df[\"salary\"]!=\"nme\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>sales</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.41</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.77</td>\n",
       "      <td>6</td>\n",
       "      <td>247</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.92</td>\n",
       "      <td>0.85</td>\n",
       "      <td>5</td>\n",
       "      <td>259</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.89</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5</td>\n",
       "      <td>224</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.42</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>142</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.45</td>\n",
       "      <td>0.54</td>\n",
       "      <td>2</td>\n",
       "      <td>135</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.81</td>\n",
       "      <td>6</td>\n",
       "      <td>305</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.84</td>\n",
       "      <td>0.92</td>\n",
       "      <td>4</td>\n",
       "      <td>234</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.41</td>\n",
       "      <td>0.55</td>\n",
       "      <td>2</td>\n",
       "      <td>148</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.36</td>\n",
       "      <td>0.56</td>\n",
       "      <td>2</td>\n",
       "      <td>137</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.54</td>\n",
       "      <td>2</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.45</td>\n",
       "      <td>0.47</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.78</td>\n",
       "      <td>0.99</td>\n",
       "      <td>4</td>\n",
       "      <td>255</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.45</td>\n",
       "      <td>0.51</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.89</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.83</td>\n",
       "      <td>6</td>\n",
       "      <td>282</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.55</td>\n",
       "      <td>2</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.09</td>\n",
       "      <td>0.95</td>\n",
       "      <td>6</td>\n",
       "      <td>304</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.46</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2</td>\n",
       "      <td>139</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.89</td>\n",
       "      <td>0.92</td>\n",
       "      <td>5</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.82</td>\n",
       "      <td>0.87</td>\n",
       "      <td>4</td>\n",
       "      <td>239</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.49</td>\n",
       "      <td>2</td>\n",
       "      <td>135</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.41</td>\n",
       "      <td>0.46</td>\n",
       "      <td>2</td>\n",
       "      <td>128</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>accounting</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2</td>\n",
       "      <td>132</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>accounting</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14969</th>\n",
       "      <td>0.43</td>\n",
       "      <td>0.46</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14970</th>\n",
       "      <td>0.78</td>\n",
       "      <td>0.93</td>\n",
       "      <td>4</td>\n",
       "      <td>225</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14971</th>\n",
       "      <td>0.39</td>\n",
       "      <td>0.45</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14972</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.97</td>\n",
       "      <td>6</td>\n",
       "      <td>310</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>accounting</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14973</th>\n",
       "      <td>0.36</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>accounting</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14974</th>\n",
       "      <td>0.36</td>\n",
       "      <td>0.54</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>accounting</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14975</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.79</td>\n",
       "      <td>7</td>\n",
       "      <td>310</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hr</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14976</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.47</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hr</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14977</th>\n",
       "      <td>0.81</td>\n",
       "      <td>0.85</td>\n",
       "      <td>4</td>\n",
       "      <td>251</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hr</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14978</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.47</td>\n",
       "      <td>2</td>\n",
       "      <td>144</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hr</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14979</th>\n",
       "      <td>0.09</td>\n",
       "      <td>0.93</td>\n",
       "      <td>6</td>\n",
       "      <td>296</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14980</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.89</td>\n",
       "      <td>5</td>\n",
       "      <td>238</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14981</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.93</td>\n",
       "      <td>5</td>\n",
       "      <td>162</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14982</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.49</td>\n",
       "      <td>2</td>\n",
       "      <td>137</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14983</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.84</td>\n",
       "      <td>5</td>\n",
       "      <td>257</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14984</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.56</td>\n",
       "      <td>2</td>\n",
       "      <td>148</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14985</th>\n",
       "      <td>0.91</td>\n",
       "      <td>0.99</td>\n",
       "      <td>5</td>\n",
       "      <td>254</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14986</th>\n",
       "      <td>0.85</td>\n",
       "      <td>0.85</td>\n",
       "      <td>4</td>\n",
       "      <td>247</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14987</th>\n",
       "      <td>0.90</td>\n",
       "      <td>0.70</td>\n",
       "      <td>5</td>\n",
       "      <td>206</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14988</th>\n",
       "      <td>0.46</td>\n",
       "      <td>0.55</td>\n",
       "      <td>2</td>\n",
       "      <td>145</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14989</th>\n",
       "      <td>0.43</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14990</th>\n",
       "      <td>0.89</td>\n",
       "      <td>0.88</td>\n",
       "      <td>5</td>\n",
       "      <td>228</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14991</th>\n",
       "      <td>0.09</td>\n",
       "      <td>0.81</td>\n",
       "      <td>6</td>\n",
       "      <td>257</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14992</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2</td>\n",
       "      <td>155</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14993</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.83</td>\n",
       "      <td>6</td>\n",
       "      <td>293</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14994</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2</td>\n",
       "      <td>151</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.96</td>\n",
       "      <td>6</td>\n",
       "      <td>280</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "0                    0.38             0.53               2   \n",
       "1                    0.80             0.86               5   \n",
       "2                    0.11             0.88               7   \n",
       "3                    0.72             0.87               5   \n",
       "4                    0.37             0.52               2   \n",
       "5                    0.41             0.50               2   \n",
       "6                    0.10             0.77               6   \n",
       "7                    0.92             0.85               5   \n",
       "8                    0.89             1.00               5   \n",
       "9                    0.42             0.53               2   \n",
       "10                   0.45             0.54               2   \n",
       "11                   0.11             0.81               6   \n",
       "12                   0.84             0.92               4   \n",
       "13                   0.41             0.55               2   \n",
       "14                   0.36             0.56               2   \n",
       "15                   0.38             0.54               2   \n",
       "16                   0.45             0.47               2   \n",
       "17                   0.78             0.99               4   \n",
       "18                   0.45             0.51               2   \n",
       "19                   0.76             0.89               5   \n",
       "20                   0.11             0.83               6   \n",
       "21                   0.38             0.55               2   \n",
       "22                   0.09             0.95               6   \n",
       "23                   0.46             0.57               2   \n",
       "24                   0.40             0.53               2   \n",
       "25                   0.89             0.92               5   \n",
       "26                   0.82             0.87               4   \n",
       "27                   0.40             0.49               2   \n",
       "28                   0.41             0.46               2   \n",
       "29                   0.38             0.50               2   \n",
       "...                   ...              ...             ...   \n",
       "14969                0.43             0.46               2   \n",
       "14970                0.78             0.93               4   \n",
       "14971                0.39             0.45               2   \n",
       "14972                0.11             0.97               6   \n",
       "14973                0.36             0.52               2   \n",
       "14974                0.36             0.54               2   \n",
       "14975                0.10             0.79               7   \n",
       "14976                0.40             0.47               2   \n",
       "14977                0.81             0.85               4   \n",
       "14978                0.40             0.47               2   \n",
       "14979                0.09             0.93               6   \n",
       "14980                0.76             0.89               5   \n",
       "14981                0.73             0.93               5   \n",
       "14982                0.38             0.49               2   \n",
       "14983                0.72             0.84               5   \n",
       "14984                0.40             0.56               2   \n",
       "14985                0.91             0.99               5   \n",
       "14986                0.85             0.85               4   \n",
       "14987                0.90             0.70               5   \n",
       "14988                0.46             0.55               2   \n",
       "14989                0.43             0.57               2   \n",
       "14990                0.89             0.88               5   \n",
       "14991                0.09             0.81               6   \n",
       "14992                0.40             0.48               2   \n",
       "14993                0.76             0.83               6   \n",
       "14994                0.40             0.57               2   \n",
       "14995                0.37             0.48               2   \n",
       "14996                0.37             0.53               2   \n",
       "14997                0.11             0.96               6   \n",
       "14998                0.37             0.52               2   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident  \\\n",
       "0                       157                   3              0   \n",
       "1                       262                   6              0   \n",
       "2                       272                   4              0   \n",
       "3                       223                   5              0   \n",
       "4                       159                   3              0   \n",
       "5                       153                   3              0   \n",
       "6                       247                   4              0   \n",
       "7                       259                   5              0   \n",
       "8                       224                   5              0   \n",
       "9                       142                   3              0   \n",
       "10                      135                   3              0   \n",
       "11                      305                   4              0   \n",
       "12                      234                   5              0   \n",
       "13                      148                   3              0   \n",
       "14                      137                   3              0   \n",
       "15                      143                   3              0   \n",
       "16                      160                   3              0   \n",
       "17                      255                   6              0   \n",
       "18                      160                   3              1   \n",
       "19                      262                   5              0   \n",
       "20                      282                   4              0   \n",
       "21                      147                   3              0   \n",
       "22                      304                   4              0   \n",
       "23                      139                   3              0   \n",
       "24                      158                   3              0   \n",
       "25                      242                   5              0   \n",
       "26                      239                   5              0   \n",
       "27                      135                   3              0   \n",
       "28                      128                   3              0   \n",
       "29                      132                   3              0   \n",
       "...                     ...                 ...            ...   \n",
       "14969                   157                   3              0   \n",
       "14970                   225                   5              0   \n",
       "14971                   140                   3              0   \n",
       "14972                   310                   4              0   \n",
       "14973                   143                   3              0   \n",
       "14974                   153                   3              0   \n",
       "14975                   310                   4              0   \n",
       "14976                   136                   3              0   \n",
       "14977                   251                   6              0   \n",
       "14978                   144                   3              0   \n",
       "14979                   296                   4              0   \n",
       "14980                   238                   5              0   \n",
       "14981                   162                   4              0   \n",
       "14982                   137                   3              0   \n",
       "14983                   257                   5              0   \n",
       "14984                   148                   3              0   \n",
       "14985                   254                   5              0   \n",
       "14986                   247                   6              0   \n",
       "14987                   206                   4              0   \n",
       "14988                   145                   3              0   \n",
       "14989                   159                   3              1   \n",
       "14990                   228                   5              1   \n",
       "14991                   257                   4              0   \n",
       "14992                   155                   3              0   \n",
       "14993                   293                   6              0   \n",
       "14994                   151                   3              0   \n",
       "14995                   160                   3              0   \n",
       "14996                   143                   3              0   \n",
       "14997                   280                   4              0   \n",
       "14998                   158                   3              0   \n",
       "\n",
       "       promotion_last_5years       sales  salary  \n",
       "0                          0       sales     low  \n",
       "1                          0       sales  medium  \n",
       "2                          0       sales  medium  \n",
       "3                          0       sales     low  \n",
       "4                          0       sales     low  \n",
       "5                          0       sales     low  \n",
       "6                          0       sales     low  \n",
       "7                          0       sales     low  \n",
       "8                          0       sales     low  \n",
       "9                          0       sales     low  \n",
       "10                         0       sales     low  \n",
       "11                         0       sales     low  \n",
       "12                         0       sales     low  \n",
       "13                         0       sales     low  \n",
       "14                         0       sales     low  \n",
       "15                         0       sales     low  \n",
       "16                         0       sales     low  \n",
       "17                         0       sales     low  \n",
       "18                         1       sales     low  \n",
       "19                         0       sales     low  \n",
       "20                         0       sales     low  \n",
       "21                         0       sales     low  \n",
       "22                         0       sales     low  \n",
       "23                         0       sales     low  \n",
       "24                         0       sales     low  \n",
       "25                         0       sales     low  \n",
       "26                         0       sales     low  \n",
       "27                         0       sales     low  \n",
       "28                         0  accounting     low  \n",
       "29                         0  accounting     low  \n",
       "...                      ...         ...     ...  \n",
       "14969                      0       sales  medium  \n",
       "14970                      0       sales  medium  \n",
       "14971                      0       sales  medium  \n",
       "14972                      0  accounting  medium  \n",
       "14973                      0  accounting  medium  \n",
       "14974                      0  accounting  medium  \n",
       "14975                      0          hr  medium  \n",
       "14976                      0          hr  medium  \n",
       "14977                      0          hr  medium  \n",
       "14978                      0          hr  medium  \n",
       "14979                      0   technical  medium  \n",
       "14980                      0   technical    high  \n",
       "14981                      0   technical     low  \n",
       "14982                      0   technical  medium  \n",
       "14983                      0   technical  medium  \n",
       "14984                      0   technical  medium  \n",
       "14985                      0   technical  medium  \n",
       "14986                      0   technical     low  \n",
       "14987                      0   technical     low  \n",
       "14988                      0   technical     low  \n",
       "14989                      0   technical     low  \n",
       "14990                      0     support     low  \n",
       "14991                      0     support     low  \n",
       "14992                      0     support     low  \n",
       "14993                      0     support     low  \n",
       "14994                      0     support     low  \n",
       "14995                      0     support     low  \n",
       "14996                      0     support     low  \n",
       "14997                      0     support     low  \n",
       "14998                      0     support     low  \n",
       "\n",
       "[14999 rows x 9 columns]"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3.特征选择\n",
    "#在探索性分析中可以看到，得出，根据相关系数，选择特征\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "#4特征处理\n",
    "# satisfaction_level，在[0,1]中，但最小不是0\n",
    "# 1，不采用\n",
    "# 2，MinMaxScaler ，进行拉伸\n",
    "# 3StandardScaler，标准化\n",
    "\n",
    "#satisfaction_level\n",
    "# last_evaluation\n",
    "# number_project\n",
    "# average_montly_hours\n",
    "#time_spend_company\n",
    "# Work_accident\n",
    "# promotion_last_5years\n",
    "# sales\n",
    "# salary\n",
    "\n",
    "#第一个属性 使用MinMaxScaler\n",
    "#第二属性 使用StandardScaler\n",
    "#第三属性：MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_lst=[\"satisfaction_level\",\"last_evaluation\",\"number_project\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['satisfaction_level', 'last_evaluation', 'number_project']"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[column_lst[0]]=\\\n",
    "            MinMaxScaler().fit_transform(df[column_lst[0]].values.reshape(-1,1)).reshape(1,-1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>sales</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.780220</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.351648</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.010989</td>\n",
       "      <td>0.77</td>\n",
       "      <td>6</td>\n",
       "      <td>247</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.912088</td>\n",
       "      <td>0.85</td>\n",
       "      <td>5</td>\n",
       "      <td>259</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.879121</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5</td>\n",
       "      <td>224</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.362637</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>142</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.395604</td>\n",
       "      <td>0.54</td>\n",
       "      <td>2</td>\n",
       "      <td>135</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.81</td>\n",
       "      <td>6</td>\n",
       "      <td>305</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.824176</td>\n",
       "      <td>0.92</td>\n",
       "      <td>4</td>\n",
       "      <td>234</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.351648</td>\n",
       "      <td>0.55</td>\n",
       "      <td>2</td>\n",
       "      <td>148</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.296703</td>\n",
       "      <td>0.56</td>\n",
       "      <td>2</td>\n",
       "      <td>137</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>0.54</td>\n",
       "      <td>2</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.395604</td>\n",
       "      <td>0.47</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.758242</td>\n",
       "      <td>0.99</td>\n",
       "      <td>4</td>\n",
       "      <td>255</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.395604</td>\n",
       "      <td>0.51</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.736264</td>\n",
       "      <td>0.89</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.83</td>\n",
       "      <td>6</td>\n",
       "      <td>282</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>0.55</td>\n",
       "      <td>2</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.95</td>\n",
       "      <td>6</td>\n",
       "      <td>304</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.406593</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2</td>\n",
       "      <td>139</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.879121</td>\n",
       "      <td>0.92</td>\n",
       "      <td>5</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.802198</td>\n",
       "      <td>0.87</td>\n",
       "      <td>4</td>\n",
       "      <td>239</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>0.49</td>\n",
       "      <td>2</td>\n",
       "      <td>135</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.351648</td>\n",
       "      <td>0.46</td>\n",
       "      <td>2</td>\n",
       "      <td>128</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>accounting</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2</td>\n",
       "      <td>132</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>accounting</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14969</th>\n",
       "      <td>0.373626</td>\n",
       "      <td>0.46</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14970</th>\n",
       "      <td>0.758242</td>\n",
       "      <td>0.93</td>\n",
       "      <td>4</td>\n",
       "      <td>225</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14971</th>\n",
       "      <td>0.329670</td>\n",
       "      <td>0.45</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14972</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.97</td>\n",
       "      <td>6</td>\n",
       "      <td>310</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>accounting</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14973</th>\n",
       "      <td>0.296703</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>accounting</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14974</th>\n",
       "      <td>0.296703</td>\n",
       "      <td>0.54</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>accounting</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14975</th>\n",
       "      <td>0.010989</td>\n",
       "      <td>0.79</td>\n",
       "      <td>7</td>\n",
       "      <td>310</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hr</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14976</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>0.47</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hr</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14977</th>\n",
       "      <td>0.791209</td>\n",
       "      <td>0.85</td>\n",
       "      <td>4</td>\n",
       "      <td>251</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hr</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14978</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>0.47</td>\n",
       "      <td>2</td>\n",
       "      <td>144</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hr</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14979</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.93</td>\n",
       "      <td>6</td>\n",
       "      <td>296</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14980</th>\n",
       "      <td>0.736264</td>\n",
       "      <td>0.89</td>\n",
       "      <td>5</td>\n",
       "      <td>238</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14981</th>\n",
       "      <td>0.703297</td>\n",
       "      <td>0.93</td>\n",
       "      <td>5</td>\n",
       "      <td>162</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14982</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>0.49</td>\n",
       "      <td>2</td>\n",
       "      <td>137</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14983</th>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.84</td>\n",
       "      <td>5</td>\n",
       "      <td>257</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14984</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>0.56</td>\n",
       "      <td>2</td>\n",
       "      <td>148</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14985</th>\n",
       "      <td>0.901099</td>\n",
       "      <td>0.99</td>\n",
       "      <td>5</td>\n",
       "      <td>254</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14986</th>\n",
       "      <td>0.835165</td>\n",
       "      <td>0.85</td>\n",
       "      <td>4</td>\n",
       "      <td>247</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14987</th>\n",
       "      <td>0.890110</td>\n",
       "      <td>0.70</td>\n",
       "      <td>5</td>\n",
       "      <td>206</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14988</th>\n",
       "      <td>0.406593</td>\n",
       "      <td>0.55</td>\n",
       "      <td>2</td>\n",
       "      <td>145</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14989</th>\n",
       "      <td>0.373626</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14990</th>\n",
       "      <td>0.879121</td>\n",
       "      <td>0.88</td>\n",
       "      <td>5</td>\n",
       "      <td>228</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14991</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.81</td>\n",
       "      <td>6</td>\n",
       "      <td>257</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14992</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2</td>\n",
       "      <td>155</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14993</th>\n",
       "      <td>0.736264</td>\n",
       "      <td>0.83</td>\n",
       "      <td>6</td>\n",
       "      <td>293</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14994</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2</td>\n",
       "      <td>151</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.96</td>\n",
       "      <td>6</td>\n",
       "      <td>280</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "0                0.318681             0.53               2   \n",
       "1                0.780220             0.86               5   \n",
       "2                0.021978             0.88               7   \n",
       "3                0.692308             0.87               5   \n",
       "4                0.307692             0.52               2   \n",
       "5                0.351648             0.50               2   \n",
       "6                0.010989             0.77               6   \n",
       "7                0.912088             0.85               5   \n",
       "8                0.879121             1.00               5   \n",
       "9                0.362637             0.53               2   \n",
       "10               0.395604             0.54               2   \n",
       "11               0.021978             0.81               6   \n",
       "12               0.824176             0.92               4   \n",
       "13               0.351648             0.55               2   \n",
       "14               0.296703             0.56               2   \n",
       "15               0.318681             0.54               2   \n",
       "16               0.395604             0.47               2   \n",
       "17               0.758242             0.99               4   \n",
       "18               0.395604             0.51               2   \n",
       "19               0.736264             0.89               5   \n",
       "20               0.021978             0.83               6   \n",
       "21               0.318681             0.55               2   \n",
       "22               0.000000             0.95               6   \n",
       "23               0.406593             0.57               2   \n",
       "24               0.340659             0.53               2   \n",
       "25               0.879121             0.92               5   \n",
       "26               0.802198             0.87               4   \n",
       "27               0.340659             0.49               2   \n",
       "28               0.351648             0.46               2   \n",
       "29               0.318681             0.50               2   \n",
       "...                   ...              ...             ...   \n",
       "14969            0.373626             0.46               2   \n",
       "14970            0.758242             0.93               4   \n",
       "14971            0.329670             0.45               2   \n",
       "14972            0.021978             0.97               6   \n",
       "14973            0.296703             0.52               2   \n",
       "14974            0.296703             0.54               2   \n",
       "14975            0.010989             0.79               7   \n",
       "14976            0.340659             0.47               2   \n",
       "14977            0.791209             0.85               4   \n",
       "14978            0.340659             0.47               2   \n",
       "14979            0.000000             0.93               6   \n",
       "14980            0.736264             0.89               5   \n",
       "14981            0.703297             0.93               5   \n",
       "14982            0.318681             0.49               2   \n",
       "14983            0.692308             0.84               5   \n",
       "14984            0.340659             0.56               2   \n",
       "14985            0.901099             0.99               5   \n",
       "14986            0.835165             0.85               4   \n",
       "14987            0.890110             0.70               5   \n",
       "14988            0.406593             0.55               2   \n",
       "14989            0.373626             0.57               2   \n",
       "14990            0.879121             0.88               5   \n",
       "14991            0.000000             0.81               6   \n",
       "14992            0.340659             0.48               2   \n",
       "14993            0.736264             0.83               6   \n",
       "14994            0.340659             0.57               2   \n",
       "14995            0.307692             0.48               2   \n",
       "14996            0.307692             0.53               2   \n",
       "14997            0.021978             0.96               6   \n",
       "14998            0.307692             0.52               2   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident  \\\n",
       "0                       157                   3              0   \n",
       "1                       262                   6              0   \n",
       "2                       272                   4              0   \n",
       "3                       223                   5              0   \n",
       "4                       159                   3              0   \n",
       "5                       153                   3              0   \n",
       "6                       247                   4              0   \n",
       "7                       259                   5              0   \n",
       "8                       224                   5              0   \n",
       "9                       142                   3              0   \n",
       "10                      135                   3              0   \n",
       "11                      305                   4              0   \n",
       "12                      234                   5              0   \n",
       "13                      148                   3              0   \n",
       "14                      137                   3              0   \n",
       "15                      143                   3              0   \n",
       "16                      160                   3              0   \n",
       "17                      255                   6              0   \n",
       "18                      160                   3              1   \n",
       "19                      262                   5              0   \n",
       "20                      282                   4              0   \n",
       "21                      147                   3              0   \n",
       "22                      304                   4              0   \n",
       "23                      139                   3              0   \n",
       "24                      158                   3              0   \n",
       "25                      242                   5              0   \n",
       "26                      239                   5              0   \n",
       "27                      135                   3              0   \n",
       "28                      128                   3              0   \n",
       "29                      132                   3              0   \n",
       "...                     ...                 ...            ...   \n",
       "14969                   157                   3              0   \n",
       "14970                   225                   5              0   \n",
       "14971                   140                   3              0   \n",
       "14972                   310                   4              0   \n",
       "14973                   143                   3              0   \n",
       "14974                   153                   3              0   \n",
       "14975                   310                   4              0   \n",
       "14976                   136                   3              0   \n",
       "14977                   251                   6              0   \n",
       "14978                   144                   3              0   \n",
       "14979                   296                   4              0   \n",
       "14980                   238                   5              0   \n",
       "14981                   162                   4              0   \n",
       "14982                   137                   3              0   \n",
       "14983                   257                   5              0   \n",
       "14984                   148                   3              0   \n",
       "14985                   254                   5              0   \n",
       "14986                   247                   6              0   \n",
       "14987                   206                   4              0   \n",
       "14988                   145                   3              0   \n",
       "14989                   159                   3              1   \n",
       "14990                   228                   5              1   \n",
       "14991                   257                   4              0   \n",
       "14992                   155                   3              0   \n",
       "14993                   293                   6              0   \n",
       "14994                   151                   3              0   \n",
       "14995                   160                   3              0   \n",
       "14996                   143                   3              0   \n",
       "14997                   280                   4              0   \n",
       "14998                   158                   3              0   \n",
       "\n",
       "       promotion_last_5years       sales  salary  \n",
       "0                          0       sales     low  \n",
       "1                          0       sales  medium  \n",
       "2                          0       sales  medium  \n",
       "3                          0       sales     low  \n",
       "4                          0       sales     low  \n",
       "5                          0       sales     low  \n",
       "6                          0       sales     low  \n",
       "7                          0       sales     low  \n",
       "8                          0       sales     low  \n",
       "9                          0       sales     low  \n",
       "10                         0       sales     low  \n",
       "11                         0       sales     low  \n",
       "12                         0       sales     low  \n",
       "13                         0       sales     low  \n",
       "14                         0       sales     low  \n",
       "15                         0       sales     low  \n",
       "16                         0       sales     low  \n",
       "17                         0       sales     low  \n",
       "18                         1       sales     low  \n",
       "19                         0       sales     low  \n",
       "20                         0       sales     low  \n",
       "21                         0       sales     low  \n",
       "22                         0       sales     low  \n",
       "23                         0       sales     low  \n",
       "24                         0       sales     low  \n",
       "25                         0       sales     low  \n",
       "26                         0       sales     low  \n",
       "27                         0       sales     low  \n",
       "28                         0  accounting     low  \n",
       "29                         0  accounting     low  \n",
       "...                      ...         ...     ...  \n",
       "14969                      0       sales  medium  \n",
       "14970                      0       sales  medium  \n",
       "14971                      0       sales  medium  \n",
       "14972                      0  accounting  medium  \n",
       "14973                      0  accounting  medium  \n",
       "14974                      0  accounting  medium  \n",
       "14975                      0          hr  medium  \n",
       "14976                      0          hr  medium  \n",
       "14977                      0          hr  medium  \n",
       "14978                      0          hr  medium  \n",
       "14979                      0   technical  medium  \n",
       "14980                      0   technical    high  \n",
       "14981                      0   technical     low  \n",
       "14982                      0   technical  medium  \n",
       "14983                      0   technical  medium  \n",
       "14984                      0   technical  medium  \n",
       "14985                      0   technical  medium  \n",
       "14986                      0   technical     low  \n",
       "14987                      0   technical     low  \n",
       "14988                      0   technical     low  \n",
       "14989                      0   technical     low  \n",
       "14990                      0     support     low  \n",
       "14991                      0     support     low  \n",
       "14992                      0     support     low  \n",
       "14993                      0     support     low  \n",
       "14994                      0     support     low  \n",
       "14995                      0     support     low  \n",
       "14996                      0     support     low  \n",
       "14997                      0     support     low  \n",
       "14998                      0     support     low  \n",
       "\n",
       "[14999 rows x 9 columns]"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[column_lst[1]]=\\\n",
    "            StandardScaler().fit_transform(df[column_lst[1]].values.reshape(-1,1)).reshape(1,-1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>sales</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-1.087275</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.780220</td>\n",
       "      <td>0.840707</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.957554</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.899131</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>-1.145699</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.351648</td>\n",
       "      <td>-1.262546</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.010989</td>\n",
       "      <td>0.314894</td>\n",
       "      <td>6</td>\n",
       "      <td>247</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.912088</td>\n",
       "      <td>0.782283</td>\n",
       "      <td>5</td>\n",
       "      <td>259</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.879121</td>\n",
       "      <td>1.658639</td>\n",
       "      <td>5</td>\n",
       "      <td>224</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.362637</td>\n",
       "      <td>-1.087275</td>\n",
       "      <td>2</td>\n",
       "      <td>142</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.395604</td>\n",
       "      <td>-1.028852</td>\n",
       "      <td>2</td>\n",
       "      <td>135</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.548588</td>\n",
       "      <td>6</td>\n",
       "      <td>305</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.824176</td>\n",
       "      <td>1.191249</td>\n",
       "      <td>4</td>\n",
       "      <td>234</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.351648</td>\n",
       "      <td>-0.970428</td>\n",
       "      <td>2</td>\n",
       "      <td>148</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.296703</td>\n",
       "      <td>-0.912004</td>\n",
       "      <td>2</td>\n",
       "      <td>137</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-1.028852</td>\n",
       "      <td>2</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.395604</td>\n",
       "      <td>-1.437818</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.758242</td>\n",
       "      <td>1.600215</td>\n",
       "      <td>4</td>\n",
       "      <td>255</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.395604</td>\n",
       "      <td>-1.204123</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.736264</td>\n",
       "      <td>1.015978</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.665436</td>\n",
       "      <td>6</td>\n",
       "      <td>282</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-0.970428</td>\n",
       "      <td>2</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.366520</td>\n",
       "      <td>6</td>\n",
       "      <td>304</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.406593</td>\n",
       "      <td>-0.853580</td>\n",
       "      <td>2</td>\n",
       "      <td>139</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.087275</td>\n",
       "      <td>2</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.879121</td>\n",
       "      <td>1.191249</td>\n",
       "      <td>5</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.802198</td>\n",
       "      <td>0.899131</td>\n",
       "      <td>4</td>\n",
       "      <td>239</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.320970</td>\n",
       "      <td>2</td>\n",
       "      <td>135</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.351648</td>\n",
       "      <td>-1.496241</td>\n",
       "      <td>2</td>\n",
       "      <td>128</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>accounting</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-1.262546</td>\n",
       "      <td>2</td>\n",
       "      <td>132</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>accounting</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14969</th>\n",
       "      <td>0.373626</td>\n",
       "      <td>-1.496241</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14970</th>\n",
       "      <td>0.758242</td>\n",
       "      <td>1.249673</td>\n",
       "      <td>4</td>\n",
       "      <td>225</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14971</th>\n",
       "      <td>0.329670</td>\n",
       "      <td>-1.554665</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14972</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>1.483368</td>\n",
       "      <td>6</td>\n",
       "      <td>310</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>accounting</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14973</th>\n",
       "      <td>0.296703</td>\n",
       "      <td>-1.145699</td>\n",
       "      <td>2</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>accounting</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14974</th>\n",
       "      <td>0.296703</td>\n",
       "      <td>-1.028852</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>accounting</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14975</th>\n",
       "      <td>0.010989</td>\n",
       "      <td>0.431741</td>\n",
       "      <td>7</td>\n",
       "      <td>310</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hr</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14976</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.437818</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hr</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14977</th>\n",
       "      <td>0.791209</td>\n",
       "      <td>0.782283</td>\n",
       "      <td>4</td>\n",
       "      <td>251</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hr</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14978</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.437818</td>\n",
       "      <td>2</td>\n",
       "      <td>144</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hr</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14979</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.249673</td>\n",
       "      <td>6</td>\n",
       "      <td>296</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14980</th>\n",
       "      <td>0.736264</td>\n",
       "      <td>1.015978</td>\n",
       "      <td>5</td>\n",
       "      <td>238</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14981</th>\n",
       "      <td>0.703297</td>\n",
       "      <td>1.249673</td>\n",
       "      <td>5</td>\n",
       "      <td>162</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14982</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-1.320970</td>\n",
       "      <td>2</td>\n",
       "      <td>137</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14983</th>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.723860</td>\n",
       "      <td>5</td>\n",
       "      <td>257</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14984</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-0.912004</td>\n",
       "      <td>2</td>\n",
       "      <td>148</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14985</th>\n",
       "      <td>0.901099</td>\n",
       "      <td>1.600215</td>\n",
       "      <td>5</td>\n",
       "      <td>254</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14986</th>\n",
       "      <td>0.835165</td>\n",
       "      <td>0.782283</td>\n",
       "      <td>4</td>\n",
       "      <td>247</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14987</th>\n",
       "      <td>0.890110</td>\n",
       "      <td>-0.094072</td>\n",
       "      <td>5</td>\n",
       "      <td>206</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14988</th>\n",
       "      <td>0.406593</td>\n",
       "      <td>-0.970428</td>\n",
       "      <td>2</td>\n",
       "      <td>145</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14989</th>\n",
       "      <td>0.373626</td>\n",
       "      <td>-0.853580</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14990</th>\n",
       "      <td>0.879121</td>\n",
       "      <td>0.957554</td>\n",
       "      <td>5</td>\n",
       "      <td>228</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14991</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.548588</td>\n",
       "      <td>6</td>\n",
       "      <td>257</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14992</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.379394</td>\n",
       "      <td>2</td>\n",
       "      <td>155</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14993</th>\n",
       "      <td>0.736264</td>\n",
       "      <td>0.665436</td>\n",
       "      <td>6</td>\n",
       "      <td>293</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14994</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-0.853580</td>\n",
       "      <td>2</td>\n",
       "      <td>151</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>-1.379394</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>-1.087275</td>\n",
       "      <td>2</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>1.424944</td>\n",
       "      <td>6</td>\n",
       "      <td>280</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>-1.145699</td>\n",
       "      <td>2</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "0                0.318681        -1.087275               2   \n",
       "1                0.780220         0.840707               5   \n",
       "2                0.021978         0.957554               7   \n",
       "3                0.692308         0.899131               5   \n",
       "4                0.307692        -1.145699               2   \n",
       "5                0.351648        -1.262546               2   \n",
       "6                0.010989         0.314894               6   \n",
       "7                0.912088         0.782283               5   \n",
       "8                0.879121         1.658639               5   \n",
       "9                0.362637        -1.087275               2   \n",
       "10               0.395604        -1.028852               2   \n",
       "11               0.021978         0.548588               6   \n",
       "12               0.824176         1.191249               4   \n",
       "13               0.351648        -0.970428               2   \n",
       "14               0.296703        -0.912004               2   \n",
       "15               0.318681        -1.028852               2   \n",
       "16               0.395604        -1.437818               2   \n",
       "17               0.758242         1.600215               4   \n",
       "18               0.395604        -1.204123               2   \n",
       "19               0.736264         1.015978               5   \n",
       "20               0.021978         0.665436               6   \n",
       "21               0.318681        -0.970428               2   \n",
       "22               0.000000         1.366520               6   \n",
       "23               0.406593        -0.853580               2   \n",
       "24               0.340659        -1.087275               2   \n",
       "25               0.879121         1.191249               5   \n",
       "26               0.802198         0.899131               4   \n",
       "27               0.340659        -1.320970               2   \n",
       "28               0.351648        -1.496241               2   \n",
       "29               0.318681        -1.262546               2   \n",
       "...                   ...              ...             ...   \n",
       "14969            0.373626        -1.496241               2   \n",
       "14970            0.758242         1.249673               4   \n",
       "14971            0.329670        -1.554665               2   \n",
       "14972            0.021978         1.483368               6   \n",
       "14973            0.296703        -1.145699               2   \n",
       "14974            0.296703        -1.028852               2   \n",
       "14975            0.010989         0.431741               7   \n",
       "14976            0.340659        -1.437818               2   \n",
       "14977            0.791209         0.782283               4   \n",
       "14978            0.340659        -1.437818               2   \n",
       "14979            0.000000         1.249673               6   \n",
       "14980            0.736264         1.015978               5   \n",
       "14981            0.703297         1.249673               5   \n",
       "14982            0.318681        -1.320970               2   \n",
       "14983            0.692308         0.723860               5   \n",
       "14984            0.340659        -0.912004               2   \n",
       "14985            0.901099         1.600215               5   \n",
       "14986            0.835165         0.782283               4   \n",
       "14987            0.890110        -0.094072               5   \n",
       "14988            0.406593        -0.970428               2   \n",
       "14989            0.373626        -0.853580               2   \n",
       "14990            0.879121         0.957554               5   \n",
       "14991            0.000000         0.548588               6   \n",
       "14992            0.340659        -1.379394               2   \n",
       "14993            0.736264         0.665436               6   \n",
       "14994            0.340659        -0.853580               2   \n",
       "14995            0.307692        -1.379394               2   \n",
       "14996            0.307692        -1.087275               2   \n",
       "14997            0.021978         1.424944               6   \n",
       "14998            0.307692        -1.145699               2   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident  \\\n",
       "0                       157                   3              0   \n",
       "1                       262                   6              0   \n",
       "2                       272                   4              0   \n",
       "3                       223                   5              0   \n",
       "4                       159                   3              0   \n",
       "5                       153                   3              0   \n",
       "6                       247                   4              0   \n",
       "7                       259                   5              0   \n",
       "8                       224                   5              0   \n",
       "9                       142                   3              0   \n",
       "10                      135                   3              0   \n",
       "11                      305                   4              0   \n",
       "12                      234                   5              0   \n",
       "13                      148                   3              0   \n",
       "14                      137                   3              0   \n",
       "15                      143                   3              0   \n",
       "16                      160                   3              0   \n",
       "17                      255                   6              0   \n",
       "18                      160                   3              1   \n",
       "19                      262                   5              0   \n",
       "20                      282                   4              0   \n",
       "21                      147                   3              0   \n",
       "22                      304                   4              0   \n",
       "23                      139                   3              0   \n",
       "24                      158                   3              0   \n",
       "25                      242                   5              0   \n",
       "26                      239                   5              0   \n",
       "27                      135                   3              0   \n",
       "28                      128                   3              0   \n",
       "29                      132                   3              0   \n",
       "...                     ...                 ...            ...   \n",
       "14969                   157                   3              0   \n",
       "14970                   225                   5              0   \n",
       "14971                   140                   3              0   \n",
       "14972                   310                   4              0   \n",
       "14973                   143                   3              0   \n",
       "14974                   153                   3              0   \n",
       "14975                   310                   4              0   \n",
       "14976                   136                   3              0   \n",
       "14977                   251                   6              0   \n",
       "14978                   144                   3              0   \n",
       "14979                   296                   4              0   \n",
       "14980                   238                   5              0   \n",
       "14981                   162                   4              0   \n",
       "14982                   137                   3              0   \n",
       "14983                   257                   5              0   \n",
       "14984                   148                   3              0   \n",
       "14985                   254                   5              0   \n",
       "14986                   247                   6              0   \n",
       "14987                   206                   4              0   \n",
       "14988                   145                   3              0   \n",
       "14989                   159                   3              1   \n",
       "14990                   228                   5              1   \n",
       "14991                   257                   4              0   \n",
       "14992                   155                   3              0   \n",
       "14993                   293                   6              0   \n",
       "14994                   151                   3              0   \n",
       "14995                   160                   3              0   \n",
       "14996                   143                   3              0   \n",
       "14997                   280                   4              0   \n",
       "14998                   158                   3              0   \n",
       "\n",
       "       promotion_last_5years       sales  salary  \n",
       "0                          0       sales     low  \n",
       "1                          0       sales  medium  \n",
       "2                          0       sales  medium  \n",
       "3                          0       sales     low  \n",
       "4                          0       sales     low  \n",
       "5                          0       sales     low  \n",
       "6                          0       sales     low  \n",
       "7                          0       sales     low  \n",
       "8                          0       sales     low  \n",
       "9                          0       sales     low  \n",
       "10                         0       sales     low  \n",
       "11                         0       sales     low  \n",
       "12                         0       sales     low  \n",
       "13                         0       sales     low  \n",
       "14                         0       sales     low  \n",
       "15                         0       sales     low  \n",
       "16                         0       sales     low  \n",
       "17                         0       sales     low  \n",
       "18                         1       sales     low  \n",
       "19                         0       sales     low  \n",
       "20                         0       sales     low  \n",
       "21                         0       sales     low  \n",
       "22                         0       sales     low  \n",
       "23                         0       sales     low  \n",
       "24                         0       sales     low  \n",
       "25                         0       sales     low  \n",
       "26                         0       sales     low  \n",
       "27                         0       sales     low  \n",
       "28                         0  accounting     low  \n",
       "29                         0  accounting     low  \n",
       "...                      ...         ...     ...  \n",
       "14969                      0       sales  medium  \n",
       "14970                      0       sales  medium  \n",
       "14971                      0       sales  medium  \n",
       "14972                      0  accounting  medium  \n",
       "14973                      0  accounting  medium  \n",
       "14974                      0  accounting  medium  \n",
       "14975                      0          hr  medium  \n",
       "14976                      0          hr  medium  \n",
       "14977                      0          hr  medium  \n",
       "14978                      0          hr  medium  \n",
       "14979                      0   technical  medium  \n",
       "14980                      0   technical    high  \n",
       "14981                      0   technical     low  \n",
       "14982                      0   technical  medium  \n",
       "14983                      0   technical  medium  \n",
       "14984                      0   technical  medium  \n",
       "14985                      0   technical  medium  \n",
       "14986                      0   technical     low  \n",
       "14987                      0   technical     low  \n",
       "14988                      0   technical     low  \n",
       "14989                      0   technical     low  \n",
       "14990                      0     support     low  \n",
       "14991                      0     support     low  \n",
       "14992                      0     support     low  \n",
       "14993                      0     support     low  \n",
       "14994                      0     support     low  \n",
       "14995                      0     support     low  \n",
       "14996                      0     support     low  \n",
       "14997                      0     support     low  \n",
       "14998                      0     support     low  \n",
       "\n",
       "[14999 rows x 9 columns]"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anacondainstall\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "df[column_lst[2]]=\\\n",
    "            MinMaxScaler().fit_transform(df[column_lst[2]].values.reshape(-1,1)).reshape(1,-1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>sales</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-1.087275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.780220</td>\n",
       "      <td>0.840707</td>\n",
       "      <td>0.6</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.957554</td>\n",
       "      <td>1.0</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.899131</td>\n",
       "      <td>0.6</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>-1.145699</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.351648</td>\n",
       "      <td>-1.262546</td>\n",
       "      <td>0.0</td>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.010989</td>\n",
       "      <td>0.314894</td>\n",
       "      <td>0.8</td>\n",
       "      <td>247</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.912088</td>\n",
       "      <td>0.782283</td>\n",
       "      <td>0.6</td>\n",
       "      <td>259</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.879121</td>\n",
       "      <td>1.658639</td>\n",
       "      <td>0.6</td>\n",
       "      <td>224</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.362637</td>\n",
       "      <td>-1.087275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>142</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.395604</td>\n",
       "      <td>-1.028852</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.548588</td>\n",
       "      <td>0.8</td>\n",
       "      <td>305</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.824176</td>\n",
       "      <td>1.191249</td>\n",
       "      <td>0.4</td>\n",
       "      <td>234</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.351648</td>\n",
       "      <td>-0.970428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>148</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.296703</td>\n",
       "      <td>-0.912004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-1.028852</td>\n",
       "      <td>0.0</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.395604</td>\n",
       "      <td>-1.437818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.758242</td>\n",
       "      <td>1.600215</td>\n",
       "      <td>0.4</td>\n",
       "      <td>255</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.395604</td>\n",
       "      <td>-1.204123</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.736264</td>\n",
       "      <td>1.015978</td>\n",
       "      <td>0.6</td>\n",
       "      <td>262</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.665436</td>\n",
       "      <td>0.8</td>\n",
       "      <td>282</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-0.970428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.366520</td>\n",
       "      <td>0.8</td>\n",
       "      <td>304</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.406593</td>\n",
       "      <td>-0.853580</td>\n",
       "      <td>0.0</td>\n",
       "      <td>139</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.087275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.879121</td>\n",
       "      <td>1.191249</td>\n",
       "      <td>0.6</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.802198</td>\n",
       "      <td>0.899131</td>\n",
       "      <td>0.4</td>\n",
       "      <td>239</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.320970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.351648</td>\n",
       "      <td>-1.496241</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>accounting</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-1.262546</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>accounting</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14969</th>\n",
       "      <td>0.373626</td>\n",
       "      <td>-1.496241</td>\n",
       "      <td>0.0</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14970</th>\n",
       "      <td>0.758242</td>\n",
       "      <td>1.249673</td>\n",
       "      <td>0.4</td>\n",
       "      <td>225</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14971</th>\n",
       "      <td>0.329670</td>\n",
       "      <td>-1.554665</td>\n",
       "      <td>0.0</td>\n",
       "      <td>140</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14972</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>1.483368</td>\n",
       "      <td>0.8</td>\n",
       "      <td>310</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>accounting</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14973</th>\n",
       "      <td>0.296703</td>\n",
       "      <td>-1.145699</td>\n",
       "      <td>0.0</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>accounting</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14974</th>\n",
       "      <td>0.296703</td>\n",
       "      <td>-1.028852</td>\n",
       "      <td>0.0</td>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>accounting</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14975</th>\n",
       "      <td>0.010989</td>\n",
       "      <td>0.431741</td>\n",
       "      <td>1.0</td>\n",
       "      <td>310</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hr</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14976</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.437818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>136</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hr</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14977</th>\n",
       "      <td>0.791209</td>\n",
       "      <td>0.782283</td>\n",
       "      <td>0.4</td>\n",
       "      <td>251</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hr</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14978</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.437818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>144</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hr</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14979</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.249673</td>\n",
       "      <td>0.8</td>\n",
       "      <td>296</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14980</th>\n",
       "      <td>0.736264</td>\n",
       "      <td>1.015978</td>\n",
       "      <td>0.6</td>\n",
       "      <td>238</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14981</th>\n",
       "      <td>0.703297</td>\n",
       "      <td>1.249673</td>\n",
       "      <td>0.6</td>\n",
       "      <td>162</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14982</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-1.320970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14983</th>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.723860</td>\n",
       "      <td>0.6</td>\n",
       "      <td>257</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14984</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-0.912004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>148</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14985</th>\n",
       "      <td>0.901099</td>\n",
       "      <td>1.600215</td>\n",
       "      <td>0.6</td>\n",
       "      <td>254</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14986</th>\n",
       "      <td>0.835165</td>\n",
       "      <td>0.782283</td>\n",
       "      <td>0.4</td>\n",
       "      <td>247</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14987</th>\n",
       "      <td>0.890110</td>\n",
       "      <td>-0.094072</td>\n",
       "      <td>0.6</td>\n",
       "      <td>206</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14988</th>\n",
       "      <td>0.406593</td>\n",
       "      <td>-0.970428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>145</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14989</th>\n",
       "      <td>0.373626</td>\n",
       "      <td>-0.853580</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14990</th>\n",
       "      <td>0.879121</td>\n",
       "      <td>0.957554</td>\n",
       "      <td>0.6</td>\n",
       "      <td>228</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14991</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.548588</td>\n",
       "      <td>0.8</td>\n",
       "      <td>257</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14992</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.379394</td>\n",
       "      <td>0.0</td>\n",
       "      <td>155</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14993</th>\n",
       "      <td>0.736264</td>\n",
       "      <td>0.665436</td>\n",
       "      <td>0.8</td>\n",
       "      <td>293</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14994</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-0.853580</td>\n",
       "      <td>0.0</td>\n",
       "      <td>151</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>-1.379394</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>-1.087275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>1.424944</td>\n",
       "      <td>0.8</td>\n",
       "      <td>280</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>-1.145699</td>\n",
       "      <td>0.0</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "0                0.318681        -1.087275             0.0   \n",
       "1                0.780220         0.840707             0.6   \n",
       "2                0.021978         0.957554             1.0   \n",
       "3                0.692308         0.899131             0.6   \n",
       "4                0.307692        -1.145699             0.0   \n",
       "5                0.351648        -1.262546             0.0   \n",
       "6                0.010989         0.314894             0.8   \n",
       "7                0.912088         0.782283             0.6   \n",
       "8                0.879121         1.658639             0.6   \n",
       "9                0.362637        -1.087275             0.0   \n",
       "10               0.395604        -1.028852             0.0   \n",
       "11               0.021978         0.548588             0.8   \n",
       "12               0.824176         1.191249             0.4   \n",
       "13               0.351648        -0.970428             0.0   \n",
       "14               0.296703        -0.912004             0.0   \n",
       "15               0.318681        -1.028852             0.0   \n",
       "16               0.395604        -1.437818             0.0   \n",
       "17               0.758242         1.600215             0.4   \n",
       "18               0.395604        -1.204123             0.0   \n",
       "19               0.736264         1.015978             0.6   \n",
       "20               0.021978         0.665436             0.8   \n",
       "21               0.318681        -0.970428             0.0   \n",
       "22               0.000000         1.366520             0.8   \n",
       "23               0.406593        -0.853580             0.0   \n",
       "24               0.340659        -1.087275             0.0   \n",
       "25               0.879121         1.191249             0.6   \n",
       "26               0.802198         0.899131             0.4   \n",
       "27               0.340659        -1.320970             0.0   \n",
       "28               0.351648        -1.496241             0.0   \n",
       "29               0.318681        -1.262546             0.0   \n",
       "...                   ...              ...             ...   \n",
       "14969            0.373626        -1.496241             0.0   \n",
       "14970            0.758242         1.249673             0.4   \n",
       "14971            0.329670        -1.554665             0.0   \n",
       "14972            0.021978         1.483368             0.8   \n",
       "14973            0.296703        -1.145699             0.0   \n",
       "14974            0.296703        -1.028852             0.0   \n",
       "14975            0.010989         0.431741             1.0   \n",
       "14976            0.340659        -1.437818             0.0   \n",
       "14977            0.791209         0.782283             0.4   \n",
       "14978            0.340659        -1.437818             0.0   \n",
       "14979            0.000000         1.249673             0.8   \n",
       "14980            0.736264         1.015978             0.6   \n",
       "14981            0.703297         1.249673             0.6   \n",
       "14982            0.318681        -1.320970             0.0   \n",
       "14983            0.692308         0.723860             0.6   \n",
       "14984            0.340659        -0.912004             0.0   \n",
       "14985            0.901099         1.600215             0.6   \n",
       "14986            0.835165         0.782283             0.4   \n",
       "14987            0.890110        -0.094072             0.6   \n",
       "14988            0.406593        -0.970428             0.0   \n",
       "14989            0.373626        -0.853580             0.0   \n",
       "14990            0.879121         0.957554             0.6   \n",
       "14991            0.000000         0.548588             0.8   \n",
       "14992            0.340659        -1.379394             0.0   \n",
       "14993            0.736264         0.665436             0.8   \n",
       "14994            0.340659        -0.853580             0.0   \n",
       "14995            0.307692        -1.379394             0.0   \n",
       "14996            0.307692        -1.087275             0.0   \n",
       "14997            0.021978         1.424944             0.8   \n",
       "14998            0.307692        -1.145699             0.0   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident  \\\n",
       "0                       157                   3              0   \n",
       "1                       262                   6              0   \n",
       "2                       272                   4              0   \n",
       "3                       223                   5              0   \n",
       "4                       159                   3              0   \n",
       "5                       153                   3              0   \n",
       "6                       247                   4              0   \n",
       "7                       259                   5              0   \n",
       "8                       224                   5              0   \n",
       "9                       142                   3              0   \n",
       "10                      135                   3              0   \n",
       "11                      305                   4              0   \n",
       "12                      234                   5              0   \n",
       "13                      148                   3              0   \n",
       "14                      137                   3              0   \n",
       "15                      143                   3              0   \n",
       "16                      160                   3              0   \n",
       "17                      255                   6              0   \n",
       "18                      160                   3              1   \n",
       "19                      262                   5              0   \n",
       "20                      282                   4              0   \n",
       "21                      147                   3              0   \n",
       "22                      304                   4              0   \n",
       "23                      139                   3              0   \n",
       "24                      158                   3              0   \n",
       "25                      242                   5              0   \n",
       "26                      239                   5              0   \n",
       "27                      135                   3              0   \n",
       "28                      128                   3              0   \n",
       "29                      132                   3              0   \n",
       "...                     ...                 ...            ...   \n",
       "14969                   157                   3              0   \n",
       "14970                   225                   5              0   \n",
       "14971                   140                   3              0   \n",
       "14972                   310                   4              0   \n",
       "14973                   143                   3              0   \n",
       "14974                   153                   3              0   \n",
       "14975                   310                   4              0   \n",
       "14976                   136                   3              0   \n",
       "14977                   251                   6              0   \n",
       "14978                   144                   3              0   \n",
       "14979                   296                   4              0   \n",
       "14980                   238                   5              0   \n",
       "14981                   162                   4              0   \n",
       "14982                   137                   3              0   \n",
       "14983                   257                   5              0   \n",
       "14984                   148                   3              0   \n",
       "14985                   254                   5              0   \n",
       "14986                   247                   6              0   \n",
       "14987                   206                   4              0   \n",
       "14988                   145                   3              0   \n",
       "14989                   159                   3              1   \n",
       "14990                   228                   5              1   \n",
       "14991                   257                   4              0   \n",
       "14992                   155                   3              0   \n",
       "14993                   293                   6              0   \n",
       "14994                   151                   3              0   \n",
       "14995                   160                   3              0   \n",
       "14996                   143                   3              0   \n",
       "14997                   280                   4              0   \n",
       "14998                   158                   3              0   \n",
       "\n",
       "       promotion_last_5years       sales  salary  \n",
       "0                          0       sales     low  \n",
       "1                          0       sales  medium  \n",
       "2                          0       sales  medium  \n",
       "3                          0       sales     low  \n",
       "4                          0       sales     low  \n",
       "5                          0       sales     low  \n",
       "6                          0       sales     low  \n",
       "7                          0       sales     low  \n",
       "8                          0       sales     low  \n",
       "9                          0       sales     low  \n",
       "10                         0       sales     low  \n",
       "11                         0       sales     low  \n",
       "12                         0       sales     low  \n",
       "13                         0       sales     low  \n",
       "14                         0       sales     low  \n",
       "15                         0       sales     low  \n",
       "16                         0       sales     low  \n",
       "17                         0       sales     low  \n",
       "18                         1       sales     low  \n",
       "19                         0       sales     low  \n",
       "20                         0       sales     low  \n",
       "21                         0       sales     low  \n",
       "22                         0       sales     low  \n",
       "23                         0       sales     low  \n",
       "24                         0       sales     low  \n",
       "25                         0       sales     low  \n",
       "26                         0       sales     low  \n",
       "27                         0       sales     low  \n",
       "28                         0  accounting     low  \n",
       "29                         0  accounting     low  \n",
       "...                      ...         ...     ...  \n",
       "14969                      0       sales  medium  \n",
       "14970                      0       sales  medium  \n",
       "14971                      0       sales  medium  \n",
       "14972                      0  accounting  medium  \n",
       "14973                      0  accounting  medium  \n",
       "14974                      0  accounting  medium  \n",
       "14975                      0          hr  medium  \n",
       "14976                      0          hr  medium  \n",
       "14977                      0          hr  medium  \n",
       "14978                      0          hr  medium  \n",
       "14979                      0   technical  medium  \n",
       "14980                      0   technical    high  \n",
       "14981                      0   technical     low  \n",
       "14982                      0   technical  medium  \n",
       "14983                      0   technical  medium  \n",
       "14984                      0   technical  medium  \n",
       "14985                      0   technical  medium  \n",
       "14986                      0   technical     low  \n",
       "14987                      0   technical     low  \n",
       "14988                      0   technical     low  \n",
       "14989                      0   technical     low  \n",
       "14990                      0     support     low  \n",
       "14991                      0     support     low  \n",
       "14992                      0     support     low  \n",
       "14993                      0     support     low  \n",
       "14994                      0     support     low  \n",
       "14995                      0     support     low  \n",
       "14996                      0     support     low  \n",
       "14997                      0     support     low  \n",
       "14998                      0     support     low  \n",
       "\n",
       "[14999 rows x 9 columns]"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "#satisfaction_level\n",
    "# last_evaluation\n",
    "# number_project\n",
    "# average_montly_hours\n",
    "#time_spend_company\n",
    "# Work_accident\n",
    "# promotion_last_5years\n",
    "# department\n",
    "# salary\n",
    "\n",
    "\n",
    "#average_montly_hours整数，并且覆盖的范围较大\n",
    "# 可以进行离散化，这里不进行离散化\n",
    "##time_spend_company\n",
    "# Work_accident\n",
    "# promotion_last_5years\n",
    "# 都可以进行类似属性1，2，3的操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "# department\n",
    "# salary\n",
    "# 离散值进行标签化\n",
    "#LabelEncoder or OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_2list=[\"sales\",\"salary\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sales', 'salary']"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_2list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anacondainstall\\lib\\site-packages\\sklearn\\preprocessing\\label.py:111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "df[column_2list[0]]=LabelEncoder().fit_transform(df[column_2list[0]].values.reshape(-1,1)).reshape(1,-1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>sales</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-1.087275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.780220</td>\n",
       "      <td>0.840707</td>\n",
       "      <td>0.6</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.957554</td>\n",
       "      <td>1.0</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.899131</td>\n",
       "      <td>0.6</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>-1.145699</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.351648</td>\n",
       "      <td>-1.262546</td>\n",
       "      <td>0.0</td>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.010989</td>\n",
       "      <td>0.314894</td>\n",
       "      <td>0.8</td>\n",
       "      <td>247</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.912088</td>\n",
       "      <td>0.782283</td>\n",
       "      <td>0.6</td>\n",
       "      <td>259</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.879121</td>\n",
       "      <td>1.658639</td>\n",
       "      <td>0.6</td>\n",
       "      <td>224</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.362637</td>\n",
       "      <td>-1.087275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>142</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.395604</td>\n",
       "      <td>-1.028852</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.548588</td>\n",
       "      <td>0.8</td>\n",
       "      <td>305</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.824176</td>\n",
       "      <td>1.191249</td>\n",
       "      <td>0.4</td>\n",
       "      <td>234</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.351648</td>\n",
       "      <td>-0.970428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>148</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.296703</td>\n",
       "      <td>-0.912004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-1.028852</td>\n",
       "      <td>0.0</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.395604</td>\n",
       "      <td>-1.437818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.758242</td>\n",
       "      <td>1.600215</td>\n",
       "      <td>0.4</td>\n",
       "      <td>255</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.395604</td>\n",
       "      <td>-1.204123</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.736264</td>\n",
       "      <td>1.015978</td>\n",
       "      <td>0.6</td>\n",
       "      <td>262</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.665436</td>\n",
       "      <td>0.8</td>\n",
       "      <td>282</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-0.970428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.366520</td>\n",
       "      <td>0.8</td>\n",
       "      <td>304</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.406593</td>\n",
       "      <td>-0.853580</td>\n",
       "      <td>0.0</td>\n",
       "      <td>139</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.087275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.879121</td>\n",
       "      <td>1.191249</td>\n",
       "      <td>0.6</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.802198</td>\n",
       "      <td>0.899131</td>\n",
       "      <td>0.4</td>\n",
       "      <td>239</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.320970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.351648</td>\n",
       "      <td>-1.496241</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-1.262546</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14969</th>\n",
       "      <td>0.373626</td>\n",
       "      <td>-1.496241</td>\n",
       "      <td>0.0</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14970</th>\n",
       "      <td>0.758242</td>\n",
       "      <td>1.249673</td>\n",
       "      <td>0.4</td>\n",
       "      <td>225</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14971</th>\n",
       "      <td>0.329670</td>\n",
       "      <td>-1.554665</td>\n",
       "      <td>0.0</td>\n",
       "      <td>140</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14972</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>1.483368</td>\n",
       "      <td>0.8</td>\n",
       "      <td>310</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14973</th>\n",
       "      <td>0.296703</td>\n",
       "      <td>-1.145699</td>\n",
       "      <td>0.0</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14974</th>\n",
       "      <td>0.296703</td>\n",
       "      <td>-1.028852</td>\n",
       "      <td>0.0</td>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14975</th>\n",
       "      <td>0.010989</td>\n",
       "      <td>0.431741</td>\n",
       "      <td>1.0</td>\n",
       "      <td>310</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14976</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.437818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>136</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14977</th>\n",
       "      <td>0.791209</td>\n",
       "      <td>0.782283</td>\n",
       "      <td>0.4</td>\n",
       "      <td>251</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14978</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.437818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>144</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14979</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.249673</td>\n",
       "      <td>0.8</td>\n",
       "      <td>296</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14980</th>\n",
       "      <td>0.736264</td>\n",
       "      <td>1.015978</td>\n",
       "      <td>0.6</td>\n",
       "      <td>238</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14981</th>\n",
       "      <td>0.703297</td>\n",
       "      <td>1.249673</td>\n",
       "      <td>0.6</td>\n",
       "      <td>162</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14982</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-1.320970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14983</th>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.723860</td>\n",
       "      <td>0.6</td>\n",
       "      <td>257</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14984</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-0.912004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>148</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14985</th>\n",
       "      <td>0.901099</td>\n",
       "      <td>1.600215</td>\n",
       "      <td>0.6</td>\n",
       "      <td>254</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14986</th>\n",
       "      <td>0.835165</td>\n",
       "      <td>0.782283</td>\n",
       "      <td>0.4</td>\n",
       "      <td>247</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14987</th>\n",
       "      <td>0.890110</td>\n",
       "      <td>-0.094072</td>\n",
       "      <td>0.6</td>\n",
       "      <td>206</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14988</th>\n",
       "      <td>0.406593</td>\n",
       "      <td>-0.970428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>145</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14989</th>\n",
       "      <td>0.373626</td>\n",
       "      <td>-0.853580</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14990</th>\n",
       "      <td>0.879121</td>\n",
       "      <td>0.957554</td>\n",
       "      <td>0.6</td>\n",
       "      <td>228</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14991</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.548588</td>\n",
       "      <td>0.8</td>\n",
       "      <td>257</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14992</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.379394</td>\n",
       "      <td>0.0</td>\n",
       "      <td>155</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14993</th>\n",
       "      <td>0.736264</td>\n",
       "      <td>0.665436</td>\n",
       "      <td>0.8</td>\n",
       "      <td>293</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14994</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-0.853580</td>\n",
       "      <td>0.0</td>\n",
       "      <td>151</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>-1.379394</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>-1.087275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>1.424944</td>\n",
       "      <td>0.8</td>\n",
       "      <td>280</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>-1.145699</td>\n",
       "      <td>0.0</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "0                0.318681        -1.087275             0.0   \n",
       "1                0.780220         0.840707             0.6   \n",
       "2                0.021978         0.957554             1.0   \n",
       "3                0.692308         0.899131             0.6   \n",
       "4                0.307692        -1.145699             0.0   \n",
       "5                0.351648        -1.262546             0.0   \n",
       "6                0.010989         0.314894             0.8   \n",
       "7                0.912088         0.782283             0.6   \n",
       "8                0.879121         1.658639             0.6   \n",
       "9                0.362637        -1.087275             0.0   \n",
       "10               0.395604        -1.028852             0.0   \n",
       "11               0.021978         0.548588             0.8   \n",
       "12               0.824176         1.191249             0.4   \n",
       "13               0.351648        -0.970428             0.0   \n",
       "14               0.296703        -0.912004             0.0   \n",
       "15               0.318681        -1.028852             0.0   \n",
       "16               0.395604        -1.437818             0.0   \n",
       "17               0.758242         1.600215             0.4   \n",
       "18               0.395604        -1.204123             0.0   \n",
       "19               0.736264         1.015978             0.6   \n",
       "20               0.021978         0.665436             0.8   \n",
       "21               0.318681        -0.970428             0.0   \n",
       "22               0.000000         1.366520             0.8   \n",
       "23               0.406593        -0.853580             0.0   \n",
       "24               0.340659        -1.087275             0.0   \n",
       "25               0.879121         1.191249             0.6   \n",
       "26               0.802198         0.899131             0.4   \n",
       "27               0.340659        -1.320970             0.0   \n",
       "28               0.351648        -1.496241             0.0   \n",
       "29               0.318681        -1.262546             0.0   \n",
       "...                   ...              ...             ...   \n",
       "14969            0.373626        -1.496241             0.0   \n",
       "14970            0.758242         1.249673             0.4   \n",
       "14971            0.329670        -1.554665             0.0   \n",
       "14972            0.021978         1.483368             0.8   \n",
       "14973            0.296703        -1.145699             0.0   \n",
       "14974            0.296703        -1.028852             0.0   \n",
       "14975            0.010989         0.431741             1.0   \n",
       "14976            0.340659        -1.437818             0.0   \n",
       "14977            0.791209         0.782283             0.4   \n",
       "14978            0.340659        -1.437818             0.0   \n",
       "14979            0.000000         1.249673             0.8   \n",
       "14980            0.736264         1.015978             0.6   \n",
       "14981            0.703297         1.249673             0.6   \n",
       "14982            0.318681        -1.320970             0.0   \n",
       "14983            0.692308         0.723860             0.6   \n",
       "14984            0.340659        -0.912004             0.0   \n",
       "14985            0.901099         1.600215             0.6   \n",
       "14986            0.835165         0.782283             0.4   \n",
       "14987            0.890110        -0.094072             0.6   \n",
       "14988            0.406593        -0.970428             0.0   \n",
       "14989            0.373626        -0.853580             0.0   \n",
       "14990            0.879121         0.957554             0.6   \n",
       "14991            0.000000         0.548588             0.8   \n",
       "14992            0.340659        -1.379394             0.0   \n",
       "14993            0.736264         0.665436             0.8   \n",
       "14994            0.340659        -0.853580             0.0   \n",
       "14995            0.307692        -1.379394             0.0   \n",
       "14996            0.307692        -1.087275             0.0   \n",
       "14997            0.021978         1.424944             0.8   \n",
       "14998            0.307692        -1.145699             0.0   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident  \\\n",
       "0                       157                   3              0   \n",
       "1                       262                   6              0   \n",
       "2                       272                   4              0   \n",
       "3                       223                   5              0   \n",
       "4                       159                   3              0   \n",
       "5                       153                   3              0   \n",
       "6                       247                   4              0   \n",
       "7                       259                   5              0   \n",
       "8                       224                   5              0   \n",
       "9                       142                   3              0   \n",
       "10                      135                   3              0   \n",
       "11                      305                   4              0   \n",
       "12                      234                   5              0   \n",
       "13                      148                   3              0   \n",
       "14                      137                   3              0   \n",
       "15                      143                   3              0   \n",
       "16                      160                   3              0   \n",
       "17                      255                   6              0   \n",
       "18                      160                   3              1   \n",
       "19                      262                   5              0   \n",
       "20                      282                   4              0   \n",
       "21                      147                   3              0   \n",
       "22                      304                   4              0   \n",
       "23                      139                   3              0   \n",
       "24                      158                   3              0   \n",
       "25                      242                   5              0   \n",
       "26                      239                   5              0   \n",
       "27                      135                   3              0   \n",
       "28                      128                   3              0   \n",
       "29                      132                   3              0   \n",
       "...                     ...                 ...            ...   \n",
       "14969                   157                   3              0   \n",
       "14970                   225                   5              0   \n",
       "14971                   140                   3              0   \n",
       "14972                   310                   4              0   \n",
       "14973                   143                   3              0   \n",
       "14974                   153                   3              0   \n",
       "14975                   310                   4              0   \n",
       "14976                   136                   3              0   \n",
       "14977                   251                   6              0   \n",
       "14978                   144                   3              0   \n",
       "14979                   296                   4              0   \n",
       "14980                   238                   5              0   \n",
       "14981                   162                   4              0   \n",
       "14982                   137                   3              0   \n",
       "14983                   257                   5              0   \n",
       "14984                   148                   3              0   \n",
       "14985                   254                   5              0   \n",
       "14986                   247                   6              0   \n",
       "14987                   206                   4              0   \n",
       "14988                   145                   3              0   \n",
       "14989                   159                   3              1   \n",
       "14990                   228                   5              1   \n",
       "14991                   257                   4              0   \n",
       "14992                   155                   3              0   \n",
       "14993                   293                   6              0   \n",
       "14994                   151                   3              0   \n",
       "14995                   160                   3              0   \n",
       "14996                   143                   3              0   \n",
       "14997                   280                   4              0   \n",
       "14998                   158                   3              0   \n",
       "\n",
       "       promotion_last_5years  sales  salary  \n",
       "0                          0      7       0  \n",
       "1                          0      7       0  \n",
       "2                          0      7       0  \n",
       "3                          0      7       0  \n",
       "4                          0      7       0  \n",
       "5                          0      7       0  \n",
       "6                          0      7       0  \n",
       "7                          0      7       0  \n",
       "8                          0      7       0  \n",
       "9                          0      7       0  \n",
       "10                         0      7       0  \n",
       "11                         0      7       0  \n",
       "12                         0      7       0  \n",
       "13                         0      7       0  \n",
       "14                         0      7       0  \n",
       "15                         0      7       0  \n",
       "16                         0      7       0  \n",
       "17                         0      7       0  \n",
       "18                         1      7       0  \n",
       "19                         0      7       0  \n",
       "20                         0      7       0  \n",
       "21                         0      7       0  \n",
       "22                         0      7       0  \n",
       "23                         0      7       0  \n",
       "24                         0      7       0  \n",
       "25                         0      7       0  \n",
       "26                         0      7       0  \n",
       "27                         0      7       0  \n",
       "28                         0      2       0  \n",
       "29                         0      2       0  \n",
       "...                      ...    ...     ...  \n",
       "14969                      0      7       0  \n",
       "14970                      0      7       0  \n",
       "14971                      0      7       0  \n",
       "14972                      0      2       0  \n",
       "14973                      0      2       0  \n",
       "14974                      0      2       0  \n",
       "14975                      0      3       0  \n",
       "14976                      0      3       0  \n",
       "14977                      0      3       0  \n",
       "14978                      0      3       0  \n",
       "14979                      0      9       0  \n",
       "14980                      0      9       0  \n",
       "14981                      0      9       0  \n",
       "14982                      0      9       0  \n",
       "14983                      0      9       0  \n",
       "14984                      0      9       0  \n",
       "14985                      0      9       0  \n",
       "14986                      0      9       0  \n",
       "14987                      0      9       0  \n",
       "14988                      0      9       0  \n",
       "14989                      0      9       0  \n",
       "14990                      0      8       0  \n",
       "14991                      0      8       0  \n",
       "14992                      0      8       0  \n",
       "14993                      0      8       0  \n",
       "14994                      0      8       0  \n",
       "14995                      0      8       0  \n",
       "14996                      0      8       0  \n",
       "14997                      0      8       0  \n",
       "14998                      0      8       0  \n",
       "\n",
       "[14999 rows x 9 columns]"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anacondainstall\\lib\\site-packages\\sklearn\\preprocessing\\label.py:111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "df[column_2list[1]]=LabelEncoder().fit_transform(df[column_2list[1]].values.reshape(-1,1)).reshape(1,-1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.318681</td>\n",
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       "      <td>0.780220</td>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.957554</td>\n",
       "      <td>1.0</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.899131</td>\n",
       "      <td>0.6</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>-1.145699</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
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       "      <td>7</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.351648</td>\n",
       "      <td>-1.262546</td>\n",
       "      <td>0.0</td>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.010989</td>\n",
       "      <td>0.314894</td>\n",
       "      <td>0.8</td>\n",
       "      <td>247</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.912088</td>\n",
       "      <td>0.782283</td>\n",
       "      <td>0.6</td>\n",
       "      <td>259</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.879121</td>\n",
       "      <td>1.658639</td>\n",
       "      <td>0.6</td>\n",
       "      <td>224</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.362637</td>\n",
       "      <td>-1.087275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>142</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.395604</td>\n",
       "      <td>-1.028852</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.548588</td>\n",
       "      <td>0.8</td>\n",
       "      <td>305</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.824176</td>\n",
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       "      <td>0.4</td>\n",
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       "      <td>5</td>\n",
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       "      <td>7</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.351648</td>\n",
       "      <td>-0.970428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>148</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.296703</td>\n",
       "      <td>-0.912004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-1.028852</td>\n",
       "      <td>0.0</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.395604</td>\n",
       "      <td>-1.437818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.758242</td>\n",
       "      <td>1.600215</td>\n",
       "      <td>0.4</td>\n",
       "      <td>255</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.395604</td>\n",
       "      <td>-1.204123</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.736264</td>\n",
       "      <td>1.015978</td>\n",
       "      <td>0.6</td>\n",
       "      <td>262</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.665436</td>\n",
       "      <td>0.8</td>\n",
       "      <td>282</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-0.970428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.366520</td>\n",
       "      <td>0.8</td>\n",
       "      <td>304</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.406593</td>\n",
       "      <td>-0.853580</td>\n",
       "      <td>0.0</td>\n",
       "      <td>139</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.087275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.879121</td>\n",
       "      <td>1.191249</td>\n",
       "      <td>0.6</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.802198</td>\n",
       "      <td>0.899131</td>\n",
       "      <td>0.4</td>\n",
       "      <td>239</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.320970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.351648</td>\n",
       "      <td>-1.496241</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-1.262546</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14969</th>\n",
       "      <td>0.373626</td>\n",
       "      <td>-1.496241</td>\n",
       "      <td>0.0</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14970</th>\n",
       "      <td>0.758242</td>\n",
       "      <td>1.249673</td>\n",
       "      <td>0.4</td>\n",
       "      <td>225</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14971</th>\n",
       "      <td>0.329670</td>\n",
       "      <td>-1.554665</td>\n",
       "      <td>0.0</td>\n",
       "      <td>140</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14972</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>1.483368</td>\n",
       "      <td>0.8</td>\n",
       "      <td>310</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14973</th>\n",
       "      <td>0.296703</td>\n",
       "      <td>-1.145699</td>\n",
       "      <td>0.0</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14974</th>\n",
       "      <td>0.296703</td>\n",
       "      <td>-1.028852</td>\n",
       "      <td>0.0</td>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14975</th>\n",
       "      <td>0.010989</td>\n",
       "      <td>0.431741</td>\n",
       "      <td>1.0</td>\n",
       "      <td>310</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14976</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.437818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>136</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14977</th>\n",
       "      <td>0.791209</td>\n",
       "      <td>0.782283</td>\n",
       "      <td>0.4</td>\n",
       "      <td>251</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14978</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.437818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>144</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14979</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.249673</td>\n",
       "      <td>0.8</td>\n",
       "      <td>296</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14980</th>\n",
       "      <td>0.736264</td>\n",
       "      <td>1.015978</td>\n",
       "      <td>0.6</td>\n",
       "      <td>238</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14981</th>\n",
       "      <td>0.703297</td>\n",
       "      <td>1.249673</td>\n",
       "      <td>0.6</td>\n",
       "      <td>162</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14982</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-1.320970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14983</th>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.723860</td>\n",
       "      <td>0.6</td>\n",
       "      <td>257</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14984</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-0.912004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>148</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14985</th>\n",
       "      <td>0.901099</td>\n",
       "      <td>1.600215</td>\n",
       "      <td>0.6</td>\n",
       "      <td>254</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14986</th>\n",
       "      <td>0.835165</td>\n",
       "      <td>0.782283</td>\n",
       "      <td>0.4</td>\n",
       "      <td>247</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14987</th>\n",
       "      <td>0.890110</td>\n",
       "      <td>-0.094072</td>\n",
       "      <td>0.6</td>\n",
       "      <td>206</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14988</th>\n",
       "      <td>0.406593</td>\n",
       "      <td>-0.970428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>145</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14989</th>\n",
       "      <td>0.373626</td>\n",
       "      <td>-0.853580</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14990</th>\n",
       "      <td>0.879121</td>\n",
       "      <td>0.957554</td>\n",
       "      <td>0.6</td>\n",
       "      <td>228</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14991</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.548588</td>\n",
       "      <td>0.8</td>\n",
       "      <td>257</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14992</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.379394</td>\n",
       "      <td>0.0</td>\n",
       "      <td>155</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14993</th>\n",
       "      <td>0.736264</td>\n",
       "      <td>0.665436</td>\n",
       "      <td>0.8</td>\n",
       "      <td>293</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14994</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-0.853580</td>\n",
       "      <td>0.0</td>\n",
       "      <td>151</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>-1.379394</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>-1.087275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>1.424944</td>\n",
       "      <td>0.8</td>\n",
       "      <td>280</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>-1.145699</td>\n",
       "      <td>0.0</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "0                0.318681        -1.087275             0.0   \n",
       "1                0.780220         0.840707             0.6   \n",
       "2                0.021978         0.957554             1.0   \n",
       "3                0.692308         0.899131             0.6   \n",
       "4                0.307692        -1.145699             0.0   \n",
       "5                0.351648        -1.262546             0.0   \n",
       "6                0.010989         0.314894             0.8   \n",
       "7                0.912088         0.782283             0.6   \n",
       "8                0.879121         1.658639             0.6   \n",
       "9                0.362637        -1.087275             0.0   \n",
       "10               0.395604        -1.028852             0.0   \n",
       "11               0.021978         0.548588             0.8   \n",
       "12               0.824176         1.191249             0.4   \n",
       "13               0.351648        -0.970428             0.0   \n",
       "14               0.296703        -0.912004             0.0   \n",
       "15               0.318681        -1.028852             0.0   \n",
       "16               0.395604        -1.437818             0.0   \n",
       "17               0.758242         1.600215             0.4   \n",
       "18               0.395604        -1.204123             0.0   \n",
       "19               0.736264         1.015978             0.6   \n",
       "20               0.021978         0.665436             0.8   \n",
       "21               0.318681        -0.970428             0.0   \n",
       "22               0.000000         1.366520             0.8   \n",
       "23               0.406593        -0.853580             0.0   \n",
       "24               0.340659        -1.087275             0.0   \n",
       "25               0.879121         1.191249             0.6   \n",
       "26               0.802198         0.899131             0.4   \n",
       "27               0.340659        -1.320970             0.0   \n",
       "28               0.351648        -1.496241             0.0   \n",
       "29               0.318681        -1.262546             0.0   \n",
       "...                   ...              ...             ...   \n",
       "14969            0.373626        -1.496241             0.0   \n",
       "14970            0.758242         1.249673             0.4   \n",
       "14971            0.329670        -1.554665             0.0   \n",
       "14972            0.021978         1.483368             0.8   \n",
       "14973            0.296703        -1.145699             0.0   \n",
       "14974            0.296703        -1.028852             0.0   \n",
       "14975            0.010989         0.431741             1.0   \n",
       "14976            0.340659        -1.437818             0.0   \n",
       "14977            0.791209         0.782283             0.4   \n",
       "14978            0.340659        -1.437818             0.0   \n",
       "14979            0.000000         1.249673             0.8   \n",
       "14980            0.736264         1.015978             0.6   \n",
       "14981            0.703297         1.249673             0.6   \n",
       "14982            0.318681        -1.320970             0.0   \n",
       "14983            0.692308         0.723860             0.6   \n",
       "14984            0.340659        -0.912004             0.0   \n",
       "14985            0.901099         1.600215             0.6   \n",
       "14986            0.835165         0.782283             0.4   \n",
       "14987            0.890110        -0.094072             0.6   \n",
       "14988            0.406593        -0.970428             0.0   \n",
       "14989            0.373626        -0.853580             0.0   \n",
       "14990            0.879121         0.957554             0.6   \n",
       "14991            0.000000         0.548588             0.8   \n",
       "14992            0.340659        -1.379394             0.0   \n",
       "14993            0.736264         0.665436             0.8   \n",
       "14994            0.340659        -0.853580             0.0   \n",
       "14995            0.307692        -1.379394             0.0   \n",
       "14996            0.307692        -1.087275             0.0   \n",
       "14997            0.021978         1.424944             0.8   \n",
       "14998            0.307692        -1.145699             0.0   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident  \\\n",
       "0                       157                   3              0   \n",
       "1                       262                   6              0   \n",
       "2                       272                   4              0   \n",
       "3                       223                   5              0   \n",
       "4                       159                   3              0   \n",
       "5                       153                   3              0   \n",
       "6                       247                   4              0   \n",
       "7                       259                   5              0   \n",
       "8                       224                   5              0   \n",
       "9                       142                   3              0   \n",
       "10                      135                   3              0   \n",
       "11                      305                   4              0   \n",
       "12                      234                   5              0   \n",
       "13                      148                   3              0   \n",
       "14                      137                   3              0   \n",
       "15                      143                   3              0   \n",
       "16                      160                   3              0   \n",
       "17                      255                   6              0   \n",
       "18                      160                   3              1   \n",
       "19                      262                   5              0   \n",
       "20                      282                   4              0   \n",
       "21                      147                   3              0   \n",
       "22                      304                   4              0   \n",
       "23                      139                   3              0   \n",
       "24                      158                   3              0   \n",
       "25                      242                   5              0   \n",
       "26                      239                   5              0   \n",
       "27                      135                   3              0   \n",
       "28                      128                   3              0   \n",
       "29                      132                   3              0   \n",
       "...                     ...                 ...            ...   \n",
       "14969                   157                   3              0   \n",
       "14970                   225                   5              0   \n",
       "14971                   140                   3              0   \n",
       "14972                   310                   4              0   \n",
       "14973                   143                   3              0   \n",
       "14974                   153                   3              0   \n",
       "14975                   310                   4              0   \n",
       "14976                   136                   3              0   \n",
       "14977                   251                   6              0   \n",
       "14978                   144                   3              0   \n",
       "14979                   296                   4              0   \n",
       "14980                   238                   5              0   \n",
       "14981                   162                   4              0   \n",
       "14982                   137                   3              0   \n",
       "14983                   257                   5              0   \n",
       "14984                   148                   3              0   \n",
       "14985                   254                   5              0   \n",
       "14986                   247                   6              0   \n",
       "14987                   206                   4              0   \n",
       "14988                   145                   3              0   \n",
       "14989                   159                   3              1   \n",
       "14990                   228                   5              1   \n",
       "14991                   257                   4              0   \n",
       "14992                   155                   3              0   \n",
       "14993                   293                   6              0   \n",
       "14994                   151                   3              0   \n",
       "14995                   160                   3              0   \n",
       "14996                   143                   3              0   \n",
       "14997                   280                   4              0   \n",
       "14998                   158                   3              0   \n",
       "\n",
       "       promotion_last_5years  sales  salary  \n",
       "0                          0      7       1  \n",
       "1                          0      7       2  \n",
       "2                          0      7       2  \n",
       "3                          0      7       1  \n",
       "4                          0      7       1  \n",
       "5                          0      7       1  \n",
       "6                          0      7       1  \n",
       "7                          0      7       1  \n",
       "8                          0      7       1  \n",
       "9                          0      7       1  \n",
       "10                         0      7       1  \n",
       "11                         0      7       1  \n",
       "12                         0      7       1  \n",
       "13                         0      7       1  \n",
       "14                         0      7       1  \n",
       "15                         0      7       1  \n",
       "16                         0      7       1  \n",
       "17                         0      7       1  \n",
       "18                         1      7       1  \n",
       "19                         0      7       1  \n",
       "20                         0      7       1  \n",
       "21                         0      7       1  \n",
       "22                         0      7       1  \n",
       "23                         0      7       1  \n",
       "24                         0      7       1  \n",
       "25                         0      7       1  \n",
       "26                         0      7       1  \n",
       "27                         0      7       1  \n",
       "28                         0      2       1  \n",
       "29                         0      2       1  \n",
       "...                      ...    ...     ...  \n",
       "14969                      0      7       2  \n",
       "14970                      0      7       2  \n",
       "14971                      0      7       2  \n",
       "14972                      0      2       2  \n",
       "14973                      0      2       2  \n",
       "14974                      0      2       2  \n",
       "14975                      0      3       2  \n",
       "14976                      0      3       2  \n",
       "14977                      0      3       2  \n",
       "14978                      0      3       2  \n",
       "14979                      0      9       2  \n",
       "14980                      0      9       0  \n",
       "14981                      0      9       1  \n",
       "14982                      0      9       2  \n",
       "14983                      0      9       2  \n",
       "14984                      0      9       2  \n",
       "14985                      0      9       2  \n",
       "14986                      0      9       1  \n",
       "14987                      0      9       1  \n",
       "14988                      0      9       1  \n",
       "14989                      0      9       1  \n",
       "14990                      0      8       1  \n",
       "14991                      0      8       1  \n",
       "14992                      0      8       1  \n",
       "14993                      0      8       1  \n",
       "14994                      0      8       1  \n",
       "14995                      0      8       1  \n",
       "14996                      0      8       1  \n",
       "14997                      0      8       1  \n",
       "14998                      0      8       1  \n",
       "\n",
       "[14999 rows x 9 columns]"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "#由于LabelEncoder对salary排序时，会对字母的手之目进行升序\n",
    "所以上面low为1.high为0\n",
    "d=dict([(\"low\",0),(\"medium\",1),(\"high\",2)])\n",
    "def map_salary(s):\n",
    "    return d.get(s,0)\n",
    "df[column_2list[1]]=[map_salary(s) for s in df[\"salary\"].values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>sales</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-1.087275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.780220</td>\n",
       "      <td>0.840707</td>\n",
       "      <td>0.6</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.957554</td>\n",
       "      <td>1.0</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.899131</td>\n",
       "      <td>0.6</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>-1.145699</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.351648</td>\n",
       "      <td>-1.262546</td>\n",
       "      <td>0.0</td>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.010989</td>\n",
       "      <td>0.314894</td>\n",
       "      <td>0.8</td>\n",
       "      <td>247</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.912088</td>\n",
       "      <td>0.782283</td>\n",
       "      <td>0.6</td>\n",
       "      <td>259</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.879121</td>\n",
       "      <td>1.658639</td>\n",
       "      <td>0.6</td>\n",
       "      <td>224</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.362637</td>\n",
       "      <td>-1.087275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>142</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.395604</td>\n",
       "      <td>-1.028852</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.548588</td>\n",
       "      <td>0.8</td>\n",
       "      <td>305</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.824176</td>\n",
       "      <td>1.191249</td>\n",
       "      <td>0.4</td>\n",
       "      <td>234</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.351648</td>\n",
       "      <td>-0.970428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>148</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.296703</td>\n",
       "      <td>-0.912004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-1.028852</td>\n",
       "      <td>0.0</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.395604</td>\n",
       "      <td>-1.437818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.758242</td>\n",
       "      <td>1.600215</td>\n",
       "      <td>0.4</td>\n",
       "      <td>255</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.395604</td>\n",
       "      <td>-1.204123</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.736264</td>\n",
       "      <td>1.015978</td>\n",
       "      <td>0.6</td>\n",
       "      <td>262</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.665436</td>\n",
       "      <td>0.8</td>\n",
       "      <td>282</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-0.970428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.366520</td>\n",
       "      <td>0.8</td>\n",
       "      <td>304</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.406593</td>\n",
       "      <td>-0.853580</td>\n",
       "      <td>0.0</td>\n",
       "      <td>139</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.087275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.879121</td>\n",
       "      <td>1.191249</td>\n",
       "      <td>0.6</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.802198</td>\n",
       "      <td>0.899131</td>\n",
       "      <td>0.4</td>\n",
       "      <td>239</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.320970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.351648</td>\n",
       "      <td>-1.496241</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-1.262546</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14969</th>\n",
       "      <td>0.373626</td>\n",
       "      <td>-1.496241</td>\n",
       "      <td>0.0</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14970</th>\n",
       "      <td>0.758242</td>\n",
       "      <td>1.249673</td>\n",
       "      <td>0.4</td>\n",
       "      <td>225</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14971</th>\n",
       "      <td>0.329670</td>\n",
       "      <td>-1.554665</td>\n",
       "      <td>0.0</td>\n",
       "      <td>140</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14972</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>1.483368</td>\n",
       "      <td>0.8</td>\n",
       "      <td>310</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14973</th>\n",
       "      <td>0.296703</td>\n",
       "      <td>-1.145699</td>\n",
       "      <td>0.0</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14974</th>\n",
       "      <td>0.296703</td>\n",
       "      <td>-1.028852</td>\n",
       "      <td>0.0</td>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14975</th>\n",
       "      <td>0.010989</td>\n",
       "      <td>0.431741</td>\n",
       "      <td>1.0</td>\n",
       "      <td>310</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14976</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.437818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>136</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14977</th>\n",
       "      <td>0.791209</td>\n",
       "      <td>0.782283</td>\n",
       "      <td>0.4</td>\n",
       "      <td>251</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14978</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.437818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>144</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14979</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.249673</td>\n",
       "      <td>0.8</td>\n",
       "      <td>296</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14980</th>\n",
       "      <td>0.736264</td>\n",
       "      <td>1.015978</td>\n",
       "      <td>0.6</td>\n",
       "      <td>238</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14981</th>\n",
       "      <td>0.703297</td>\n",
       "      <td>1.249673</td>\n",
       "      <td>0.6</td>\n",
       "      <td>162</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14982</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-1.320970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14983</th>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.723860</td>\n",
       "      <td>0.6</td>\n",
       "      <td>257</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14984</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-0.912004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>148</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14985</th>\n",
       "      <td>0.901099</td>\n",
       "      <td>1.600215</td>\n",
       "      <td>0.6</td>\n",
       "      <td>254</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14986</th>\n",
       "      <td>0.835165</td>\n",
       "      <td>0.782283</td>\n",
       "      <td>0.4</td>\n",
       "      <td>247</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14987</th>\n",
       "      <td>0.890110</td>\n",
       "      <td>-0.094072</td>\n",
       "      <td>0.6</td>\n",
       "      <td>206</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14988</th>\n",
       "      <td>0.406593</td>\n",
       "      <td>-0.970428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>145</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14989</th>\n",
       "      <td>0.373626</td>\n",
       "      <td>-0.853580</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14990</th>\n",
       "      <td>0.879121</td>\n",
       "      <td>0.957554</td>\n",
       "      <td>0.6</td>\n",
       "      <td>228</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14991</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.548588</td>\n",
       "      <td>0.8</td>\n",
       "      <td>257</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14992</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.379394</td>\n",
       "      <td>0.0</td>\n",
       "      <td>155</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14993</th>\n",
       "      <td>0.736264</td>\n",
       "      <td>0.665436</td>\n",
       "      <td>0.8</td>\n",
       "      <td>293</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14994</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-0.853580</td>\n",
       "      <td>0.0</td>\n",
       "      <td>151</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>-1.379394</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>-1.087275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>1.424944</td>\n",
       "      <td>0.8</td>\n",
       "      <td>280</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>-1.145699</td>\n",
       "      <td>0.0</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "0                0.318681        -1.087275             0.0   \n",
       "1                0.780220         0.840707             0.6   \n",
       "2                0.021978         0.957554             1.0   \n",
       "3                0.692308         0.899131             0.6   \n",
       "4                0.307692        -1.145699             0.0   \n",
       "5                0.351648        -1.262546             0.0   \n",
       "6                0.010989         0.314894             0.8   \n",
       "7                0.912088         0.782283             0.6   \n",
       "8                0.879121         1.658639             0.6   \n",
       "9                0.362637        -1.087275             0.0   \n",
       "10               0.395604        -1.028852             0.0   \n",
       "11               0.021978         0.548588             0.8   \n",
       "12               0.824176         1.191249             0.4   \n",
       "13               0.351648        -0.970428             0.0   \n",
       "14               0.296703        -0.912004             0.0   \n",
       "15               0.318681        -1.028852             0.0   \n",
       "16               0.395604        -1.437818             0.0   \n",
       "17               0.758242         1.600215             0.4   \n",
       "18               0.395604        -1.204123             0.0   \n",
       "19               0.736264         1.015978             0.6   \n",
       "20               0.021978         0.665436             0.8   \n",
       "21               0.318681        -0.970428             0.0   \n",
       "22               0.000000         1.366520             0.8   \n",
       "23               0.406593        -0.853580             0.0   \n",
       "24               0.340659        -1.087275             0.0   \n",
       "25               0.879121         1.191249             0.6   \n",
       "26               0.802198         0.899131             0.4   \n",
       "27               0.340659        -1.320970             0.0   \n",
       "28               0.351648        -1.496241             0.0   \n",
       "29               0.318681        -1.262546             0.0   \n",
       "...                   ...              ...             ...   \n",
       "14969            0.373626        -1.496241             0.0   \n",
       "14970            0.758242         1.249673             0.4   \n",
       "14971            0.329670        -1.554665             0.0   \n",
       "14972            0.021978         1.483368             0.8   \n",
       "14973            0.296703        -1.145699             0.0   \n",
       "14974            0.296703        -1.028852             0.0   \n",
       "14975            0.010989         0.431741             1.0   \n",
       "14976            0.340659        -1.437818             0.0   \n",
       "14977            0.791209         0.782283             0.4   \n",
       "14978            0.340659        -1.437818             0.0   \n",
       "14979            0.000000         1.249673             0.8   \n",
       "14980            0.736264         1.015978             0.6   \n",
       "14981            0.703297         1.249673             0.6   \n",
       "14982            0.318681        -1.320970             0.0   \n",
       "14983            0.692308         0.723860             0.6   \n",
       "14984            0.340659        -0.912004             0.0   \n",
       "14985            0.901099         1.600215             0.6   \n",
       "14986            0.835165         0.782283             0.4   \n",
       "14987            0.890110        -0.094072             0.6   \n",
       "14988            0.406593        -0.970428             0.0   \n",
       "14989            0.373626        -0.853580             0.0   \n",
       "14990            0.879121         0.957554             0.6   \n",
       "14991            0.000000         0.548588             0.8   \n",
       "14992            0.340659        -1.379394             0.0   \n",
       "14993            0.736264         0.665436             0.8   \n",
       "14994            0.340659        -0.853580             0.0   \n",
       "14995            0.307692        -1.379394             0.0   \n",
       "14996            0.307692        -1.087275             0.0   \n",
       "14997            0.021978         1.424944             0.8   \n",
       "14998            0.307692        -1.145699             0.0   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident  \\\n",
       "0                       157                   3              0   \n",
       "1                       262                   6              0   \n",
       "2                       272                   4              0   \n",
       "3                       223                   5              0   \n",
       "4                       159                   3              0   \n",
       "5                       153                   3              0   \n",
       "6                       247                   4              0   \n",
       "7                       259                   5              0   \n",
       "8                       224                   5              0   \n",
       "9                       142                   3              0   \n",
       "10                      135                   3              0   \n",
       "11                      305                   4              0   \n",
       "12                      234                   5              0   \n",
       "13                      148                   3              0   \n",
       "14                      137                   3              0   \n",
       "15                      143                   3              0   \n",
       "16                      160                   3              0   \n",
       "17                      255                   6              0   \n",
       "18                      160                   3              1   \n",
       "19                      262                   5              0   \n",
       "20                      282                   4              0   \n",
       "21                      147                   3              0   \n",
       "22                      304                   4              0   \n",
       "23                      139                   3              0   \n",
       "24                      158                   3              0   \n",
       "25                      242                   5              0   \n",
       "26                      239                   5              0   \n",
       "27                      135                   3              0   \n",
       "28                      128                   3              0   \n",
       "29                      132                   3              0   \n",
       "...                     ...                 ...            ...   \n",
       "14969                   157                   3              0   \n",
       "14970                   225                   5              0   \n",
       "14971                   140                   3              0   \n",
       "14972                   310                   4              0   \n",
       "14973                   143                   3              0   \n",
       "14974                   153                   3              0   \n",
       "14975                   310                   4              0   \n",
       "14976                   136                   3              0   \n",
       "14977                   251                   6              0   \n",
       "14978                   144                   3              0   \n",
       "14979                   296                   4              0   \n",
       "14980                   238                   5              0   \n",
       "14981                   162                   4              0   \n",
       "14982                   137                   3              0   \n",
       "14983                   257                   5              0   \n",
       "14984                   148                   3              0   \n",
       "14985                   254                   5              0   \n",
       "14986                   247                   6              0   \n",
       "14987                   206                   4              0   \n",
       "14988                   145                   3              0   \n",
       "14989                   159                   3              1   \n",
       "14990                   228                   5              1   \n",
       "14991                   257                   4              0   \n",
       "14992                   155                   3              0   \n",
       "14993                   293                   6              0   \n",
       "14994                   151                   3              0   \n",
       "14995                   160                   3              0   \n",
       "14996                   143                   3              0   \n",
       "14997                   280                   4              0   \n",
       "14998                   158                   3              0   \n",
       "\n",
       "       promotion_last_5years  sales  salary  \n",
       "0                          0      7       0  \n",
       "1                          0      7       0  \n",
       "2                          0      7       0  \n",
       "3                          0      7       0  \n",
       "4                          0      7       0  \n",
       "5                          0      7       0  \n",
       "6                          0      7       0  \n",
       "7                          0      7       0  \n",
       "8                          0      7       0  \n",
       "9                          0      7       0  \n",
       "10                         0      7       0  \n",
       "11                         0      7       0  \n",
       "12                         0      7       0  \n",
       "13                         0      7       0  \n",
       "14                         0      7       0  \n",
       "15                         0      7       0  \n",
       "16                         0      7       0  \n",
       "17                         0      7       0  \n",
       "18                         1      7       0  \n",
       "19                         0      7       0  \n",
       "20                         0      7       0  \n",
       "21                         0      7       0  \n",
       "22                         0      7       0  \n",
       "23                         0      7       0  \n",
       "24                         0      7       0  \n",
       "25                         0      7       0  \n",
       "26                         0      7       0  \n",
       "27                         0      7       0  \n",
       "28                         0      2       0  \n",
       "29                         0      2       0  \n",
       "...                      ...    ...     ...  \n",
       "14969                      0      7       0  \n",
       "14970                      0      7       0  \n",
       "14971                      0      7       0  \n",
       "14972                      0      2       0  \n",
       "14973                      0      2       0  \n",
       "14974                      0      2       0  \n",
       "14975                      0      3       0  \n",
       "14976                      0      3       0  \n",
       "14977                      0      3       0  \n",
       "14978                      0      3       0  \n",
       "14979                      0      9       0  \n",
       "14980                      0      9       0  \n",
       "14981                      0      9       0  \n",
       "14982                      0      9       0  \n",
       "14983                      0      9       0  \n",
       "14984                      0      9       0  \n",
       "14985                      0      9       0  \n",
       "14986                      0      9       0  \n",
       "14987                      0      9       0  \n",
       "14988                      0      9       0  \n",
       "14989                      0      9       0  \n",
       "14990                      0      8       0  \n",
       "14991                      0      8       0  \n",
       "14992                      0      8       0  \n",
       "14993                      0      8       0  \n",
       "14994                      0      8       0  \n",
       "14995                      0      8       0  \n",
       "14996                      0      8       0  \n",
       "14997                      0      8       0  \n",
       "14998                      0      8       0  \n",
       "\n",
       "[14999 rows x 9 columns]"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "labels ['sales'] not contained in axis",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-232-7731db687949>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#OneHotEncoder,对sales进行LabelEncoder后，在进行OneHotEncoder\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_dummies\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcolumn_2list\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\anacondainstall\\lib\\site-packages\\pandas\\core\\reshape\\reshape.py\u001b[0m in \u001b[0;36mget_dummies\u001b[1;34m(data, prefix, prefix_sep, dummy_na, columns, sparse, drop_first)\u001b[0m\n\u001b[0;32m   1202\u001b[0m             \u001b[0mwith_dummies\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1203\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1204\u001b[1;33m             \u001b[0mwith_dummies\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns_to_encode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1205\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1206\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpre\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msep\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns_to_encode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprefix\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprefix_sep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anacondainstall\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   2528\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2529\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2530\u001b[1;33m                 \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2531\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2532\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anacondainstall\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[1;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[0;32m   2560\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2561\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2562\u001b[1;33m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2563\u001b[0m             \u001b[0mdropped\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2564\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anacondainstall\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   3742\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m'ignore'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3743\u001b[0m                 raise ValueError('labels %s not contained in axis' %\n\u001b[1;32m-> 3744\u001b[1;33m                                  labels[mask])\n\u001b[0m\u001b[0;32m   3745\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3746\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: labels ['sales'] not contained in axis"
     ]
    }
   ],
   "source": [
    "#OneHotEncoder,对sales进行LabelEncoder后，在进行OneHotEncoder\n",
    "df=pd.get_dummies(df,columns=[column_2list[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.351648</td>\n",
       "      <td>-1.262546</td>\n",
       "      <td>0.0</td>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.010989</td>\n",
       "      <td>0.314894</td>\n",
       "      <td>0.8</td>\n",
       "      <td>247</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.912088</td>\n",
       "      <td>0.782283</td>\n",
       "      <td>0.6</td>\n",
       "      <td>259</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.879121</td>\n",
       "      <td>1.658639</td>\n",
       "      <td>0.6</td>\n",
       "      <td>224</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.362637</td>\n",
       "      <td>-1.087275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>142</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.395604</td>\n",
       "      <td>-1.028852</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.548588</td>\n",
       "      <td>0.8</td>\n",
       "      <td>305</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.824176</td>\n",
       "      <td>1.191249</td>\n",
       "      <td>0.4</td>\n",
       "      <td>234</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.351648</td>\n",
       "      <td>-0.970428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>148</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.296703</td>\n",
       "      <td>-0.912004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-1.028852</td>\n",
       "      <td>0.0</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.395604</td>\n",
       "      <td>-1.437818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.758242</td>\n",
       "      <td>1.600215</td>\n",
       "      <td>0.4</td>\n",
       "      <td>255</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.395604</td>\n",
       "      <td>-1.204123</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.736264</td>\n",
       "      <td>1.015978</td>\n",
       "      <td>0.6</td>\n",
       "      <td>262</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.665436</td>\n",
       "      <td>0.8</td>\n",
       "      <td>282</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-0.970428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.366520</td>\n",
       "      <td>0.8</td>\n",
       "      <td>304</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.406593</td>\n",
       "      <td>-0.853580</td>\n",
       "      <td>0.0</td>\n",
       "      <td>139</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.087275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.879121</td>\n",
       "      <td>1.191249</td>\n",
       "      <td>0.6</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.802198</td>\n",
       "      <td>0.899131</td>\n",
       "      <td>0.4</td>\n",
       "      <td>239</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.320970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.351648</td>\n",
       "      <td>-1.496241</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-1.262546</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14969</th>\n",
       "      <td>0.373626</td>\n",
       "      <td>-1.496241</td>\n",
       "      <td>0.0</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14970</th>\n",
       "      <td>0.758242</td>\n",
       "      <td>1.249673</td>\n",
       "      <td>0.4</td>\n",
       "      <td>225</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14971</th>\n",
       "      <td>0.329670</td>\n",
       "      <td>-1.554665</td>\n",
       "      <td>0.0</td>\n",
       "      <td>140</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14972</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>1.483368</td>\n",
       "      <td>0.8</td>\n",
       "      <td>310</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14973</th>\n",
       "      <td>0.296703</td>\n",
       "      <td>-1.145699</td>\n",
       "      <td>0.0</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14974</th>\n",
       "      <td>0.296703</td>\n",
       "      <td>-1.028852</td>\n",
       "      <td>0.0</td>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14975</th>\n",
       "      <td>0.010989</td>\n",
       "      <td>0.431741</td>\n",
       "      <td>1.0</td>\n",
       "      <td>310</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14976</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.437818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>136</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14977</th>\n",
       "      <td>0.791209</td>\n",
       "      <td>0.782283</td>\n",
       "      <td>0.4</td>\n",
       "      <td>251</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14978</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.437818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>144</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14979</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.249673</td>\n",
       "      <td>0.8</td>\n",
       "      <td>296</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14980</th>\n",
       "      <td>0.736264</td>\n",
       "      <td>1.015978</td>\n",
       "      <td>0.6</td>\n",
       "      <td>238</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14981</th>\n",
       "      <td>0.703297</td>\n",
       "      <td>1.249673</td>\n",
       "      <td>0.6</td>\n",
       "      <td>162</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14982</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>-1.320970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14983</th>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.723860</td>\n",
       "      <td>0.6</td>\n",
       "      <td>257</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14984</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-0.912004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>148</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14985</th>\n",
       "      <td>0.901099</td>\n",
       "      <td>1.600215</td>\n",
       "      <td>0.6</td>\n",
       "      <td>254</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14986</th>\n",
       "      <td>0.835165</td>\n",
       "      <td>0.782283</td>\n",
       "      <td>0.4</td>\n",
       "      <td>247</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14987</th>\n",
       "      <td>0.890110</td>\n",
       "      <td>-0.094072</td>\n",
       "      <td>0.6</td>\n",
       "      <td>206</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14988</th>\n",
       "      <td>0.406593</td>\n",
       "      <td>-0.970428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>145</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14989</th>\n",
       "      <td>0.373626</td>\n",
       "      <td>-0.853580</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14990</th>\n",
       "      <td>0.879121</td>\n",
       "      <td>0.957554</td>\n",
       "      <td>0.6</td>\n",
       "      <td>228</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14991</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.548588</td>\n",
       "      <td>0.8</td>\n",
       "      <td>257</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14992</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-1.379394</td>\n",
       "      <td>0.0</td>\n",
       "      <td>155</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14993</th>\n",
       "      <td>0.736264</td>\n",
       "      <td>0.665436</td>\n",
       "      <td>0.8</td>\n",
       "      <td>293</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14994</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>-0.853580</td>\n",
       "      <td>0.0</td>\n",
       "      <td>151</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>-1.379394</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>-1.087275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>1.424944</td>\n",
       "      <td>0.8</td>\n",
       "      <td>280</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>-1.145699</td>\n",
       "      <td>0.0</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "0                0.318681        -1.087275             0.0   \n",
       "1                0.780220         0.840707             0.6   \n",
       "2                0.021978         0.957554             1.0   \n",
       "3                0.692308         0.899131             0.6   \n",
       "4                0.307692        -1.145699             0.0   \n",
       "5                0.351648        -1.262546             0.0   \n",
       "6                0.010989         0.314894             0.8   \n",
       "7                0.912088         0.782283             0.6   \n",
       "8                0.879121         1.658639             0.6   \n",
       "9                0.362637        -1.087275             0.0   \n",
       "10               0.395604        -1.028852             0.0   \n",
       "11               0.021978         0.548588             0.8   \n",
       "12               0.824176         1.191249             0.4   \n",
       "13               0.351648        -0.970428             0.0   \n",
       "14               0.296703        -0.912004             0.0   \n",
       "15               0.318681        -1.028852             0.0   \n",
       "16               0.395604        -1.437818             0.0   \n",
       "17               0.758242         1.600215             0.4   \n",
       "18               0.395604        -1.204123             0.0   \n",
       "19               0.736264         1.015978             0.6   \n",
       "20               0.021978         0.665436             0.8   \n",
       "21               0.318681        -0.970428             0.0   \n",
       "22               0.000000         1.366520             0.8   \n",
       "23               0.406593        -0.853580             0.0   \n",
       "24               0.340659        -1.087275             0.0   \n",
       "25               0.879121         1.191249             0.6   \n",
       "26               0.802198         0.899131             0.4   \n",
       "27               0.340659        -1.320970             0.0   \n",
       "28               0.351648        -1.496241             0.0   \n",
       "29               0.318681        -1.262546             0.0   \n",
       "...                   ...              ...             ...   \n",
       "14969            0.373626        -1.496241             0.0   \n",
       "14970            0.758242         1.249673             0.4   \n",
       "14971            0.329670        -1.554665             0.0   \n",
       "14972            0.021978         1.483368             0.8   \n",
       "14973            0.296703        -1.145699             0.0   \n",
       "14974            0.296703        -1.028852             0.0   \n",
       "14975            0.010989         0.431741             1.0   \n",
       "14976            0.340659        -1.437818             0.0   \n",
       "14977            0.791209         0.782283             0.4   \n",
       "14978            0.340659        -1.437818             0.0   \n",
       "14979            0.000000         1.249673             0.8   \n",
       "14980            0.736264         1.015978             0.6   \n",
       "14981            0.703297         1.249673             0.6   \n",
       "14982            0.318681        -1.320970             0.0   \n",
       "14983            0.692308         0.723860             0.6   \n",
       "14984            0.340659        -0.912004             0.0   \n",
       "14985            0.901099         1.600215             0.6   \n",
       "14986            0.835165         0.782283             0.4   \n",
       "14987            0.890110        -0.094072             0.6   \n",
       "14988            0.406593        -0.970428             0.0   \n",
       "14989            0.373626        -0.853580             0.0   \n",
       "14990            0.879121         0.957554             0.6   \n",
       "14991            0.000000         0.548588             0.8   \n",
       "14992            0.340659        -1.379394             0.0   \n",
       "14993            0.736264         0.665436             0.8   \n",
       "14994            0.340659        -0.853580             0.0   \n",
       "14995            0.307692        -1.379394             0.0   \n",
       "14996            0.307692        -1.087275             0.0   \n",
       "14997            0.021978         1.424944             0.8   \n",
       "14998            0.307692        -1.145699             0.0   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident  \\\n",
       "0                       157                   3              0   \n",
       "1                       262                   6              0   \n",
       "2                       272                   4              0   \n",
       "3                       223                   5              0   \n",
       "4                       159                   3              0   \n",
       "5                       153                   3              0   \n",
       "6                       247                   4              0   \n",
       "7                       259                   5              0   \n",
       "8                       224                   5              0   \n",
       "9                       142                   3              0   \n",
       "10                      135                   3              0   \n",
       "11                      305                   4              0   \n",
       "12                      234                   5              0   \n",
       "13                      148                   3              0   \n",
       "14                      137                   3              0   \n",
       "15                      143                   3              0   \n",
       "16                      160                   3              0   \n",
       "17                      255                   6              0   \n",
       "18                      160                   3              1   \n",
       "19                      262                   5              0   \n",
       "20                      282                   4              0   \n",
       "21                      147                   3              0   \n",
       "22                      304                   4              0   \n",
       "23                      139                   3              0   \n",
       "24                      158                   3              0   \n",
       "25                      242                   5              0   \n",
       "26                      239                   5              0   \n",
       "27                      135                   3              0   \n",
       "28                      128                   3              0   \n",
       "29                      132                   3              0   \n",
       "...                     ...                 ...            ...   \n",
       "14969                   157                   3              0   \n",
       "14970                   225                   5              0   \n",
       "14971                   140                   3              0   \n",
       "14972                   310                   4              0   \n",
       "14973                   143                   3              0   \n",
       "14974                   153                   3              0   \n",
       "14975                   310                   4              0   \n",
       "14976                   136                   3              0   \n",
       "14977                   251                   6              0   \n",
       "14978                   144                   3              0   \n",
       "14979                   296                   4              0   \n",
       "14980                   238                   5              0   \n",
       "14981                   162                   4              0   \n",
       "14982                   137                   3              0   \n",
       "14983                   257                   5              0   \n",
       "14984                   148                   3              0   \n",
       "14985                   254                   5              0   \n",
       "14986                   247                   6              0   \n",
       "14987                   206                   4              0   \n",
       "14988                   145                   3              0   \n",
       "14989                   159                   3              1   \n",
       "14990                   228                   5              1   \n",
       "14991                   257                   4              0   \n",
       "14992                   155                   3              0   \n",
       "14993                   293                   6              0   \n",
       "14994                   151                   3              0   \n",
       "14995                   160                   3              0   \n",
       "14996                   143                   3              0   \n",
       "14997                   280                   4              0   \n",
       "14998                   158                   3              0   \n",
       "\n",
       "       promotion_last_5years  salary  sales_0  sales_1  sales_2  sales_3  \\\n",
       "0                          0       0        0        0        0        0   \n",
       "1                          0       0        0        0        0        0   \n",
       "2                          0       0        0        0        0        0   \n",
       "3                          0       0        0        0        0        0   \n",
       "4                          0       0        0        0        0        0   \n",
       "5                          0       0        0        0        0        0   \n",
       "6                          0       0        0        0        0        0   \n",
       "7                          0       0        0        0        0        0   \n",
       "8                          0       0        0        0        0        0   \n",
       "9                          0       0        0        0        0        0   \n",
       "10                         0       0        0        0        0        0   \n",
       "11                         0       0        0        0        0        0   \n",
       "12                         0       0        0        0        0        0   \n",
       "13                         0       0        0        0        0        0   \n",
       "14                         0       0        0        0        0        0   \n",
       "15                         0       0        0        0        0        0   \n",
       "16                         0       0        0        0        0        0   \n",
       "17                         0       0        0        0        0        0   \n",
       "18                         1       0        0        0        0        0   \n",
       "19                         0       0        0        0        0        0   \n",
       "20                         0       0        0        0        0        0   \n",
       "21                         0       0        0        0        0        0   \n",
       "22                         0       0        0        0        0        0   \n",
       "23                         0       0        0        0        0        0   \n",
       "24                         0       0        0        0        0        0   \n",
       "25                         0       0        0        0        0        0   \n",
       "26                         0       0        0        0        0        0   \n",
       "27                         0       0        0        0        0        0   \n",
       "28                         0       0        0        0        1        0   \n",
       "29                         0       0        0        0        1        0   \n",
       "...                      ...     ...      ...      ...      ...      ...   \n",
       "14969                      0       0        0        0        0        0   \n",
       "14970                      0       0        0        0        0        0   \n",
       "14971                      0       0        0        0        0        0   \n",
       "14972                      0       0        0        0        1        0   \n",
       "14973                      0       0        0        0        1        0   \n",
       "14974                      0       0        0        0        1        0   \n",
       "14975                      0       0        0        0        0        1   \n",
       "14976                      0       0        0        0        0        1   \n",
       "14977                      0       0        0        0        0        1   \n",
       "14978                      0       0        0        0        0        1   \n",
       "14979                      0       0        0        0        0        0   \n",
       "14980                      0       0        0        0        0        0   \n",
       "14981                      0       0        0        0        0        0   \n",
       "14982                      0       0        0        0        0        0   \n",
       "14983                      0       0        0        0        0        0   \n",
       "14984                      0       0        0        0        0        0   \n",
       "14985                      0       0        0        0        0        0   \n",
       "14986                      0       0        0        0        0        0   \n",
       "14987                      0       0        0        0        0        0   \n",
       "14988                      0       0        0        0        0        0   \n",
       "14989                      0       0        0        0        0        0   \n",
       "14990                      0       0        0        0        0        0   \n",
       "14991                      0       0        0        0        0        0   \n",
       "14992                      0       0        0        0        0        0   \n",
       "14993                      0       0        0        0        0        0   \n",
       "14994                      0       0        0        0        0        0   \n",
       "14995                      0       0        0        0        0        0   \n",
       "14996                      0       0        0        0        0        0   \n",
       "14997                      0       0        0        0        0        0   \n",
       "14998                      0       0        0        0        0        0   \n",
       "\n",
       "       sales_4  sales_5  sales_6  sales_7  sales_8  sales_9  \n",
       "0            0        0        0        1        0        0  \n",
       "1            0        0        0        1        0        0  \n",
       "2            0        0        0        1        0        0  \n",
       "3            0        0        0        1        0        0  \n",
       "4            0        0        0        1        0        0  \n",
       "5            0        0        0        1        0        0  \n",
       "6            0        0        0        1        0        0  \n",
       "7            0        0        0        1        0        0  \n",
       "8            0        0        0        1        0        0  \n",
       "9            0        0        0        1        0        0  \n",
       "10           0        0        0        1        0        0  \n",
       "11           0        0        0        1        0        0  \n",
       "12           0        0        0        1        0        0  \n",
       "13           0        0        0        1        0        0  \n",
       "14           0        0        0        1        0        0  \n",
       "15           0        0        0        1        0        0  \n",
       "16           0        0        0        1        0        0  \n",
       "17           0        0        0        1        0        0  \n",
       "18           0        0        0        1        0        0  \n",
       "19           0        0        0        1        0        0  \n",
       "20           0        0        0        1        0        0  \n",
       "21           0        0        0        1        0        0  \n",
       "22           0        0        0        1        0        0  \n",
       "23           0        0        0        1        0        0  \n",
       "24           0        0        0        1        0        0  \n",
       "25           0        0        0        1        0        0  \n",
       "26           0        0        0        1        0        0  \n",
       "27           0        0        0        1        0        0  \n",
       "28           0        0        0        0        0        0  \n",
       "29           0        0        0        0        0        0  \n",
       "...        ...      ...      ...      ...      ...      ...  \n",
       "14969        0        0        0        1        0        0  \n",
       "14970        0        0        0        1        0        0  \n",
       "14971        0        0        0        1        0        0  \n",
       "14972        0        0        0        0        0        0  \n",
       "14973        0        0        0        0        0        0  \n",
       "14974        0        0        0        0        0        0  \n",
       "14975        0        0        0        0        0        0  \n",
       "14976        0        0        0        0        0        0  \n",
       "14977        0        0        0        0        0        0  \n",
       "14978        0        0        0        0        0        0  \n",
       "14979        0        0        0        0        0        1  \n",
       "14980        0        0        0        0        0        1  \n",
       "14981        0        0        0        0        0        1  \n",
       "14982        0        0        0        0        0        1  \n",
       "14983        0        0        0        0        0        1  \n",
       "14984        0        0        0        0        0        1  \n",
       "14985        0        0        0        0        0        1  \n",
       "14986        0        0        0        0        0        1  \n",
       "14987        0        0        0        0        0        1  \n",
       "14988        0        0        0        0        0        1  \n",
       "14989        0        0        0        0        0        1  \n",
       "14990        0        0        0        0        1        0  \n",
       "14991        0        0        0        0        1        0  \n",
       "14992        0        0        0        0        1        0  \n",
       "14993        0        0        0        0        1        0  \n",
       "14994        0        0        0        0        1        0  \n",
       "14995        0        0        0        0        1        0  \n",
       "14996        0        0        0        0        1        0  \n",
       "14997        0        0        0        0        1        0  \n",
       "14998        0        0        0        0        1        0  \n",
       "\n",
       "[14999 rows x 18 columns]"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "#判断是否降维\n",
    "from sklearn.decomposition import PCA\n",
    "#特征降维"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=PCA(n_components=3).fit_transform(df.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-44.0589052 ,  -0.41116863,   0.78729382],\n",
       "       [ 60.96322601,   2.309197  ,  -0.18329277],\n",
       "       [ 70.9571284 ,   0.31354713,  -0.43415634],\n",
       "       ...,\n",
       "       [-58.05845406,  -0.36057462,   0.66822562],\n",
       "       [ 78.95963691,   0.30869155,  -0.86409041],\n",
       "       [-43.05933589,  -0.4339683 ,   0.8231582 ]])"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1        1\n",
       "2        1\n",
       "3        1\n",
       "4        1\n",
       "5        1\n",
       "6        1\n",
       "7        1\n",
       "8        1\n",
       "9        1\n",
       "10       1\n",
       "11       1\n",
       "12       1\n",
       "13       1\n",
       "14       1\n",
       "15       1\n",
       "16       1\n",
       "17       1\n",
       "18       1\n",
       "19       1\n",
       "20       1\n",
       "21       1\n",
       "22       1\n",
       "23       1\n",
       "24       1\n",
       "25       1\n",
       "26       1\n",
       "27       1\n",
       "28       1\n",
       "29       1\n",
       "        ..\n",
       "14969    1\n",
       "14970    1\n",
       "14971    1\n",
       "14972    1\n",
       "14973    1\n",
       "14974    1\n",
       "14975    1\n",
       "14976    1\n",
       "14977    1\n",
       "14978    1\n",
       "14979    1\n",
       "14980    1\n",
       "14981    1\n",
       "14982    1\n",
       "14983    1\n",
       "14984    1\n",
       "14985    1\n",
       "14986    1\n",
       "14987    1\n",
       "14988    1\n",
       "14989    1\n",
       "14990    1\n",
       "14991    1\n",
       "14992    1\n",
       "14993    1\n",
       "14994    1\n",
       "14995    1\n",
       "14996    1\n",
       "14997    1\n",
       "14998    1\n",
       "Name: left, Length: 14999, dtype: int64"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anacondainstall\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "D:\\anacondainstall\\lib\\site-packages\\sklearn\\preprocessing\\label.py:111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import MinMaxScaler,StandardScaler\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.preprocessing import Normalizer\n",
    "#判断是否降维\n",
    "from sklearn.decomposition import PCA\n",
    "#特征降维\n",
    "\n",
    "#sl:MinMaxScaler:False,StandardScaler：true\n",
    "#le:MinMaxScaler:False,StandardScaler：true\n",
    "# nup::MinMaxScaler:False,StandardScaler：true\n",
    "#......\n",
    "#.........\n",
    "\n",
    "# sa：#LabelEncoder ：False OneHotEncoder：true\n",
    "#sal:#LabelEncoder ：False OneHotEncoder：true\n",
    "d=dict([(\"low\",0),(\"medium\",1),(\"high\",2)])\n",
    "def map_salary(s):\n",
    "    return d.get(s,0)\n",
    "\n",
    "def hr_processing(sl=False,le=False,nup= False,amh=False,tsc=False,wa=False,\\\n",
    "                  pl5=False,sa=False,sal=False,lower=False,id_n=3):\n",
    "    new_df = pd.read_csv('HR.csv')\n",
    "    #1清洗数据（数据较少，不抽样操作）\n",
    "    new_df=new_df.dropna(subset=[\"satisfaction_level\",\"last_evaluation\"])\n",
    "    new_df=new_df[new_df[\"satisfaction_level\"]<=1][new_df[\"salary\"]!=\"nme\"]\n",
    "    #2得到标注---left\n",
    "    label=new_df[\"left\"]\n",
    "    new_df = new_df.drop('left', axis=1)\n",
    "    #3特征变换\n",
    "    column_lst=[\"satisfaction_level\",\"last_evaluation\",\"number_project\",\\\n",
    "                \"average_montly_hours\",\"time_spend_company\",\"Work_accident\",\\\n",
    "                \"promotion_last_5years\"]\n",
    "    scaler_1st=[sl,le,nup,amh,tsc,wa,pl5]\n",
    "\n",
    "    for i in range(len(scaler_1st)):\n",
    "        if not scaler_1st[i]:\n",
    "            new_df[column_lst[i]]=\\\n",
    "            MinMaxScaler().fit_transform(new_df[column_lst[i]].values.reshape(-1,1)).reshape(1,-1)[0]\n",
    "        else :\n",
    "            new_df[column_lst[i]]=\\\n",
    "            StandardScaler().fit_transform(new_df[column_lst[i]].values.reshape(-1,1)).reshape(1,-1)[0]\n",
    "    \n",
    "    #数值化\n",
    "    \n",
    "    column_2list=[\"sales\",\"salary\"]\n",
    "    scaler_2st=[sa,sal]\n",
    "\n",
    "    for ii in range(len(scaler_2st)):\n",
    "        if not scaler_2st[ii]:\n",
    "            if column_2list[ii]==\"salary\":\n",
    "                new_df[column_2list[ii ]]=[map_salary(s) for s in new_df[\"salary\"].values]\n",
    "            else:\n",
    "                new_df[column_2list[ii]]=LabelEncoder().fit_transform(new_df[column_2list[ii]].\\\n",
    "                                                                      values.reshape(-1,1)).reshape(1,-1)[0]\n",
    "                #归一化处理\n",
    "            new_df[column_2list[ii]]=\\\n",
    "            MinMaxScaler().fit_transform(new_df[column_2list[ii]].values.reshape(-1,1)).reshape(1,-1)[0]\n",
    "            \n",
    "        else:\n",
    "            new_df=pd.get_dummies(new_df,columns=[column_2list[ii]])\n",
    "    if lower:#是否降维\n",
    "        new_df=PCA(n_components=id_n).fit_transform(new_df.values)\n",
    "    return new_df,label\n",
    "    \n",
    "\n",
    "features,labels=hr_processing(sa=False,lower=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>sales</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>0.265625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.285047</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.780220</td>\n",
       "      <td>0.781250</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.775701</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.822430</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.796875</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.593458</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.294393</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.351648</td>\n",
       "      <td>0.218750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.266355</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.010989</td>\n",
       "      <td>0.640625</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.705607</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.912088</td>\n",
       "      <td>0.765625</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.761682</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.879121</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.598131</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.362637</td>\n",
       "      <td>0.265625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.214953</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.395604</td>\n",
       "      <td>0.281250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.182243</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.703125</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.976636</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.824176</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.644860</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.351648</td>\n",
       "      <td>0.296875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.242991</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.296703</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.191589</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>0.281250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.219626</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.395604</td>\n",
       "      <td>0.171875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.299065</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.758242</td>\n",
       "      <td>0.984375</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.742991</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.395604</td>\n",
       "      <td>0.234375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.299065</td>\n",
       "      <td>0.125</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.736264</td>\n",
       "      <td>0.828125</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.775701</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.734375</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.869159</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>0.296875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.238318</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.921875</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.971963</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.406593</td>\n",
       "      <td>0.328125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.200935</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>0.265625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.289720</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.879121</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.682243</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.802198</td>\n",
       "      <td>0.796875</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.668224</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>0.203125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.182243</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.351648</td>\n",
       "      <td>0.156250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149533</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>0.218750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.168224</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14969</th>\n",
       "      <td>0.373626</td>\n",
       "      <td>0.156250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.285047</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14970</th>\n",
       "      <td>0.758242</td>\n",
       "      <td>0.890625</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.602804</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14971</th>\n",
       "      <td>0.329670</td>\n",
       "      <td>0.140625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.205607</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14972</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.953125</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14973</th>\n",
       "      <td>0.296703</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.219626</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14974</th>\n",
       "      <td>0.296703</td>\n",
       "      <td>0.281250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.266355</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14975</th>\n",
       "      <td>0.010989</td>\n",
       "      <td>0.671875</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14976</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>0.171875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.186916</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14977</th>\n",
       "      <td>0.791209</td>\n",
       "      <td>0.765625</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.724299</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14978</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>0.171875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.224299</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14979</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.890625</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.934579</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14980</th>\n",
       "      <td>0.736264</td>\n",
       "      <td>0.828125</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.663551</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14981</th>\n",
       "      <td>0.703297</td>\n",
       "      <td>0.890625</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.308411</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14982</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>0.203125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.191589</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14983</th>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.752336</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14984</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.242991</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14985</th>\n",
       "      <td>0.901099</td>\n",
       "      <td>0.984375</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.738318</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14986</th>\n",
       "      <td>0.835165</td>\n",
       "      <td>0.765625</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.705607</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14987</th>\n",
       "      <td>0.890110</td>\n",
       "      <td>0.531250</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.514019</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14988</th>\n",
       "      <td>0.406593</td>\n",
       "      <td>0.296875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.228972</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14989</th>\n",
       "      <td>0.373626</td>\n",
       "      <td>0.328125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.294393</td>\n",
       "      <td>0.125</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14990</th>\n",
       "      <td>0.879121</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.616822</td>\n",
       "      <td>0.375</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14991</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.703125</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.752336</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14992</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.275701</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14993</th>\n",
       "      <td>0.736264</td>\n",
       "      <td>0.734375</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.920561</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14994</th>\n",
       "      <td>0.340659</td>\n",
       "      <td>0.328125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.257009</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.299065</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.265625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.219626</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.859813</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.289720</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "0                0.318681         0.265625             0.0   \n",
       "1                0.780220         0.781250             0.6   \n",
       "2                0.021978         0.812500             1.0   \n",
       "3                0.692308         0.796875             0.6   \n",
       "4                0.307692         0.250000             0.0   \n",
       "5                0.351648         0.218750             0.0   \n",
       "6                0.010989         0.640625             0.8   \n",
       "7                0.912088         0.765625             0.6   \n",
       "8                0.879121         1.000000             0.6   \n",
       "9                0.362637         0.265625             0.0   \n",
       "10               0.395604         0.281250             0.0   \n",
       "11               0.021978         0.703125             0.8   \n",
       "12               0.824176         0.875000             0.4   \n",
       "13               0.351648         0.296875             0.0   \n",
       "14               0.296703         0.312500             0.0   \n",
       "15               0.318681         0.281250             0.0   \n",
       "16               0.395604         0.171875             0.0   \n",
       "17               0.758242         0.984375             0.4   \n",
       "18               0.395604         0.234375             0.0   \n",
       "19               0.736264         0.828125             0.6   \n",
       "20               0.021978         0.734375             0.8   \n",
       "21               0.318681         0.296875             0.0   \n",
       "22               0.000000         0.921875             0.8   \n",
       "23               0.406593         0.328125             0.0   \n",
       "24               0.340659         0.265625             0.0   \n",
       "25               0.879121         0.875000             0.6   \n",
       "26               0.802198         0.796875             0.4   \n",
       "27               0.340659         0.203125             0.0   \n",
       "28               0.351648         0.156250             0.0   \n",
       "29               0.318681         0.218750             0.0   \n",
       "...                   ...              ...             ...   \n",
       "14969            0.373626         0.156250             0.0   \n",
       "14970            0.758242         0.890625             0.4   \n",
       "14971            0.329670         0.140625             0.0   \n",
       "14972            0.021978         0.953125             0.8   \n",
       "14973            0.296703         0.250000             0.0   \n",
       "14974            0.296703         0.281250             0.0   \n",
       "14975            0.010989         0.671875             1.0   \n",
       "14976            0.340659         0.171875             0.0   \n",
       "14977            0.791209         0.765625             0.4   \n",
       "14978            0.340659         0.171875             0.0   \n",
       "14979            0.000000         0.890625             0.8   \n",
       "14980            0.736264         0.828125             0.6   \n",
       "14981            0.703297         0.890625             0.6   \n",
       "14982            0.318681         0.203125             0.0   \n",
       "14983            0.692308         0.750000             0.6   \n",
       "14984            0.340659         0.312500             0.0   \n",
       "14985            0.901099         0.984375             0.6   \n",
       "14986            0.835165         0.765625             0.4   \n",
       "14987            0.890110         0.531250             0.6   \n",
       "14988            0.406593         0.296875             0.0   \n",
       "14989            0.373626         0.328125             0.0   \n",
       "14990            0.879121         0.812500             0.6   \n",
       "14991            0.000000         0.703125             0.8   \n",
       "14992            0.340659         0.187500             0.0   \n",
       "14993            0.736264         0.734375             0.8   \n",
       "14994            0.340659         0.328125             0.0   \n",
       "14995            0.307692         0.187500             0.0   \n",
       "14996            0.307692         0.265625             0.0   \n",
       "14997            0.021978         0.937500             0.8   \n",
       "14998            0.307692         0.250000             0.0   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident  \\\n",
       "0                  0.285047               0.125            0.0   \n",
       "1                  0.775701               0.500            0.0   \n",
       "2                  0.822430               0.250            0.0   \n",
       "3                  0.593458               0.375            0.0   \n",
       "4                  0.294393               0.125            0.0   \n",
       "5                  0.266355               0.125            0.0   \n",
       "6                  0.705607               0.250            0.0   \n",
       "7                  0.761682               0.375            0.0   \n",
       "8                  0.598131               0.375            0.0   \n",
       "9                  0.214953               0.125            0.0   \n",
       "10                 0.182243               0.125            0.0   \n",
       "11                 0.976636               0.250            0.0   \n",
       "12                 0.644860               0.375            0.0   \n",
       "13                 0.242991               0.125            0.0   \n",
       "14                 0.191589               0.125            0.0   \n",
       "15                 0.219626               0.125            0.0   \n",
       "16                 0.299065               0.125            0.0   \n",
       "17                 0.742991               0.500            0.0   \n",
       "18                 0.299065               0.125            1.0   \n",
       "19                 0.775701               0.375            0.0   \n",
       "20                 0.869159               0.250            0.0   \n",
       "21                 0.238318               0.125            0.0   \n",
       "22                 0.971963               0.250            0.0   \n",
       "23                 0.200935               0.125            0.0   \n",
       "24                 0.289720               0.125            0.0   \n",
       "25                 0.682243               0.375            0.0   \n",
       "26                 0.668224               0.375            0.0   \n",
       "27                 0.182243               0.125            0.0   \n",
       "28                 0.149533               0.125            0.0   \n",
       "29                 0.168224               0.125            0.0   \n",
       "...                     ...                 ...            ...   \n",
       "14969              0.285047               0.125            0.0   \n",
       "14970              0.602804               0.375            0.0   \n",
       "14971              0.205607               0.125            0.0   \n",
       "14972              1.000000               0.250            0.0   \n",
       "14973              0.219626               0.125            0.0   \n",
       "14974              0.266355               0.125            0.0   \n",
       "14975              1.000000               0.250            0.0   \n",
       "14976              0.186916               0.125            0.0   \n",
       "14977              0.724299               0.500            0.0   \n",
       "14978              0.224299               0.125            0.0   \n",
       "14979              0.934579               0.250            0.0   \n",
       "14980              0.663551               0.375            0.0   \n",
       "14981              0.308411               0.250            0.0   \n",
       "14982              0.191589               0.125            0.0   \n",
       "14983              0.752336               0.375            0.0   \n",
       "14984              0.242991               0.125            0.0   \n",
       "14985              0.738318               0.375            0.0   \n",
       "14986              0.705607               0.500            0.0   \n",
       "14987              0.514019               0.250            0.0   \n",
       "14988              0.228972               0.125            0.0   \n",
       "14989              0.294393               0.125            1.0   \n",
       "14990              0.616822               0.375            1.0   \n",
       "14991              0.752336               0.250            0.0   \n",
       "14992              0.275701               0.125            0.0   \n",
       "14993              0.920561               0.500            0.0   \n",
       "14994              0.257009               0.125            0.0   \n",
       "14995              0.299065               0.125            0.0   \n",
       "14996              0.219626               0.125            0.0   \n",
       "14997              0.859813               0.250            0.0   \n",
       "14998              0.289720               0.125            0.0   \n",
       "\n",
       "       promotion_last_5years     sales  salary  \n",
       "0                        0.0  0.777778     0.0  \n",
       "1                        0.0  0.777778     0.5  \n",
       "2                        0.0  0.777778     0.5  \n",
       "3                        0.0  0.777778     0.0  \n",
       "4                        0.0  0.777778     0.0  \n",
       "5                        0.0  0.777778     0.0  \n",
       "6                        0.0  0.777778     0.0  \n",
       "7                        0.0  0.777778     0.0  \n",
       "8                        0.0  0.777778     0.0  \n",
       "9                        0.0  0.777778     0.0  \n",
       "10                       0.0  0.777778     0.0  \n",
       "11                       0.0  0.777778     0.0  \n",
       "12                       0.0  0.777778     0.0  \n",
       "13                       0.0  0.777778     0.0  \n",
       "14                       0.0  0.777778     0.0  \n",
       "15                       0.0  0.777778     0.0  \n",
       "16                       0.0  0.777778     0.0  \n",
       "17                       0.0  0.777778     0.0  \n",
       "18                       1.0  0.777778     0.0  \n",
       "19                       0.0  0.777778     0.0  \n",
       "20                       0.0  0.777778     0.0  \n",
       "21                       0.0  0.777778     0.0  \n",
       "22                       0.0  0.777778     0.0  \n",
       "23                       0.0  0.777778     0.0  \n",
       "24                       0.0  0.777778     0.0  \n",
       "25                       0.0  0.777778     0.0  \n",
       "26                       0.0  0.777778     0.0  \n",
       "27                       0.0  0.777778     0.0  \n",
       "28                       0.0  0.222222     0.0  \n",
       "29                       0.0  0.222222     0.0  \n",
       "...                      ...       ...     ...  \n",
       "14969                    0.0  0.777778     0.5  \n",
       "14970                    0.0  0.777778     0.5  \n",
       "14971                    0.0  0.777778     0.5  \n",
       "14972                    0.0  0.222222     0.5  \n",
       "14973                    0.0  0.222222     0.5  \n",
       "14974                    0.0  0.222222     0.5  \n",
       "14975                    0.0  0.333333     0.5  \n",
       "14976                    0.0  0.333333     0.5  \n",
       "14977                    0.0  0.333333     0.5  \n",
       "14978                    0.0  0.333333     0.5  \n",
       "14979                    0.0  1.000000     0.5  \n",
       "14980                    0.0  1.000000     1.0  \n",
       "14981                    0.0  1.000000     0.0  \n",
       "14982                    0.0  1.000000     0.5  \n",
       "14983                    0.0  1.000000     0.5  \n",
       "14984                    0.0  1.000000     0.5  \n",
       "14985                    0.0  1.000000     0.5  \n",
       "14986                    0.0  1.000000     0.0  \n",
       "14987                    0.0  1.000000     0.0  \n",
       "14988                    0.0  1.000000     0.0  \n",
       "14989                    0.0  1.000000     0.0  \n",
       "14990                    0.0  0.888889     0.0  \n",
       "14991                    0.0  0.888889     0.0  \n",
       "14992                    0.0  0.888889     0.0  \n",
       "14993                    0.0  0.888889     0.0  \n",
       "14994                    0.0  0.888889     0.0  \n",
       "14995                    0.0  0.888889     0.0  \n",
       "14996                    0.0  0.888889     0.0  \n",
       "14997                    0.0  0.888889     0.0  \n",
       "14998                    0.0  0.888889     0.0  \n",
       "\n",
       "[14999 rows x 9 columns]"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1        1\n",
       "2        1\n",
       "3        1\n",
       "4        1\n",
       "5        1\n",
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       "7        1\n",
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       "9        1\n",
       "10       1\n",
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       "26       1\n",
       "27       1\n",
       "28       1\n",
       "29       1\n",
       "        ..\n",
       "14969    1\n",
       "14970    1\n",
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       "14973    1\n",
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       "14980    1\n",
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       "14985    1\n",
       "14986    1\n",
       "14987    1\n",
       "14988    1\n",
       "14989    1\n",
       "14990    1\n",
       "14991    1\n",
       "14992    1\n",
       "14993    1\n",
       "14994    1\n",
       "14995    1\n",
       "14996    1\n",
       "14997    1\n",
       "14998    1\n",
       "Name: left, Length: 14999, dtype: int64"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 机器学习与模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8999 3000 3000\n"
     ]
    }
   ],
   "source": [
    "#只能在dataframe，才能使用\n",
    "def hr_modeling(features,labels):\n",
    "    from sklearn.model_selection import train_test_split\n",
    "    f_v=features.values\n",
    "    l_v=labels.values\n",
    "    X_tt,X_validation,Y_tt,Y_validation = train_test_split(f_v,l_v,test_size = 0.2)#训练集和验证集\n",
    "    x_train,x_test,y_train,y_test = train_test_split(X_tt,Y_tt,test_size = 0.25)#\n",
    "    print(len(x_train),len(y_test),len(Y_validation))\n",
    "    \n",
    "    return  x_train,x_test,y_train,y_test,X_validation,Y_validation\n",
    "x_tra,x_tes,y_tra,y_tes,x_val,y_val=hr_modeling(features,labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 分类模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Knn判别模型\n",
    "from sklearn.neighbors import NearestNeighbors,KNeighborsClassifier\n",
    "#NearestNeighbors直接获得点附近的点数\n",
    "#官网查询n_neighbors可以修改\n",
    "knn_cls=KNeighborsClassifier(n_neighbors=3)#n_neighbors可以修改"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=3, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_cls.fit(x_tra,y_tra)#拟合数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pre=knn_cls.predict(x_val)#\n",
    "from sklearn.metrics import accuracy_score,recall_score,f1_score#加载评价的标准"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc: 0.948\n",
      "Recall: 0.9376770538243626\n",
      "F1_score: 0.8945945945945946\n"
     ]
    }
   ],
   "source": [
    "print(\"Acc:\",accuracy_score(y_val,y_pre))\n",
    "print(\"Recall:\",recall_score(y_val,y_pre))\n",
    "print(\"F1_score:\",f1_score(y_val,y_pre))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['knn_cls']"
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals import joblib#保存模型\n",
    "joblib.dump(knn_cls,\"knn_cls\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc: 0.948\n",
      "Recall: 0.9376770538243626\n",
      "F1_score: 0.8945945945945946\n"
     ]
    }
   ],
   "source": [
    "knn_cls=joblib.load(\"knn_cls\")#加载模型\n",
    "\n",
    "y_pre2=knn_cls.predict(x_val)\n",
    "\n",
    "print(\"Acc:\",accuracy_score(y_val,y_pre2))\n",
    "print(\"Recall:\",recall_score(y_val,y_pre2))\n",
    "print(\"F1_score:\",f1_score(y_val,y_pre2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "............KNN....0..........\n",
      "Acc: 0.9787754194910545\n",
      "Recall: 0.9636948529411765\n",
      "F1_score: 0.956442417331813\n",
      "............KNN....1..........\n",
      "Acc: 0.9553333333333334\n",
      "Recall: 0.93033381712627\n",
      "F1_score: 0.9053672316384181\n",
      "............KNN....2..........\n",
      "Acc: 0.948\n",
      "Recall: 0.9376770538243626\n",
      "F1_score: 0.8945945945945946\n",
      "............GaussianNB....0..........\n",
      "Acc: 0.7989776641849095\n",
      "Recall: 0.7362132352941176\n",
      "F1_score: 0.6391382405745063\n",
      "............GaussianNB....1..........\n",
      "Acc: 0.7946666666666666\n",
      "Recall: 0.7111756168359942\n",
      "F1_score: 0.6140350877192983\n",
      "............GaussianNB....2..........\n",
      "Acc: 0.786\n",
      "Recall: 0.7294617563739377\n",
      "F1_score: 0.6160287081339714\n",
      "............BernoulliNB....0..........\n",
      "Acc: 0.8427603067007445\n",
      "Recall: 0.47702205882352944\n",
      "F1_score: 0.594672013749642\n",
      "............BernoulliNB....1..........\n",
      "Acc: 0.8373333333333334\n",
      "Recall: 0.42380261248185774\n",
      "F1_score: 0.5447761194029851\n",
      "............BernoulliNB....2..........\n",
      "Acc: 0.8423333333333334\n",
      "Recall: 0.49008498583569404\n",
      "F1_score: 0.5939914163090129\n",
      "............DecisionTreeClassifier....0..........\n",
      "Acc: 1.0\n",
      "Recall: 1.0\n",
      "F1_score: 1.0\n",
      "............DecisionTreeClassifier....1..........\n",
      "Acc: 0.9776666666666667\n",
      "Recall: 0.9709724238026125\n",
      "F1_score: 0.9523131672597864\n",
      "............DecisionTreeClassifier....2..........\n",
      "Acc: 0.9743333333333334\n",
      "Recall: 0.9688385269121813\n",
      "F1_score: 0.9467128027681662\n",
      "............SVM classifier....0..........\n",
      "Acc: 0.9145460606734082\n",
      "Recall: 0.7564338235294118\n",
      "F1_score: 0.8106377739473037\n",
      "............SVM classifier....1..........\n",
      "Acc: 0.9136666666666666\n",
      "Recall: 0.7300435413642961\n",
      "F1_score: 0.7952569169960474\n",
      "............SVM classifier....2..........\n",
      "Acc: 0.9153333333333333\n",
      "Recall: 0.7549575070821529\n",
      "F1_score: 0.8075757575757575\n",
      "............RandForetClassifier....0..........\n",
      "Acc: 0.9981109012112457\n",
      "Recall: 0.9926470588235294\n",
      "F1_score: 0.9960802397970948\n",
      "............RandForetClassifier....1..........\n",
      "Acc: 0.9893333333333333\n",
      "Recall: 0.9622641509433962\n",
      "F1_score: 0.9764359351988218\n",
      "............RandForetClassifier....2..........\n",
      "Acc: 0.988\n",
      "Recall: 0.9589235127478754\n",
      "F1_score: 0.9741007194244604\n",
      "............AdaBoostClassifier....0..........\n",
      "Acc: 0.7581953550394488\n",
      "Recall: 0.0\n",
      "F1_score: 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anacondainstall\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "............AdaBoostClassifier....1..........\n",
      "Acc: 0.7703333333333333\n",
      "Recall: 0.0\n",
      "F1_score: 0.0\n",
      "............AdaBoostClassifier....2..........\n",
      "Acc: 0.7646666666666667\n",
      "Recall: 0.0\n",
      "F1_score: 0.0\n",
      "............LogisticRegression....0..........\n",
      "Acc: 0.7889765529503279\n",
      "Recall: 0.359375\n",
      "F1_score: 0.4516315333525845\n",
      "............LogisticRegression....1..........\n",
      "Acc: 0.7986666666666666\n",
      "Recall: 0.3613933236574746\n",
      "F1_score: 0.4519056261343013\n",
      "............LogisticRegression....2..........\n",
      "Acc: 0.7983333333333333\n",
      "Recall: 0.3810198300283286\n",
      "F1_score: 0.47069116360454943\n",
      "............GBTD....0..........\n",
      "Acc: 0.994332703633737\n",
      "Recall: 0.9788602941176471\n",
      "F1_score: 0.9881697981906751\n",
      "............GBTD....1..........\n",
      "Acc: 0.9866666666666667\n",
      "Recall: 0.9550072568940493\n",
      "F1_score: 0.9705014749262538\n",
      "............GBTD....2..........\n",
      "Acc: 0.986\n",
      "Recall: 0.9546742209631728\n",
      "F1_score: 0.9697841726618704\n"
     ]
    }
   ],
   "source": [
    "#整合网络结构\n",
    "from sklearn.metrics import accuracy_score,recall_score,f1_score#加载评价的标准\n",
    "models=[]\n",
    "models.append((\"KNN\",KNeighborsClassifier(n_neighbors=3)))\n",
    "models.append((\"GaussianNB\",GaussianNB()))\n",
    "models.append((\"BernoulliNB\",BernoulliNB()))\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "models.append((\"DecisionTreeClassifier\",DecisionTreeClassifier()))\n",
    "from sklearn.svm import SVC\n",
    "models.append((\"SVM classifier\",SVC()))\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "models.append((\"RandForetClassifier\",RandomForestClassifier()))\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "models.append((\"AdaBoostClassifier\",AdaBoostClassifier(n_estimators =1000,base_estimator=SVC(),algorithm='SAMME' )))\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "models.append((\"LogisticRegression\",LogisticRegression(C=1000,tol=1e-10,)))\n",
    "# C,指代与支持向量机一样，较小的值指定更强的正则化。\n",
    "#tol：精度\n",
    "#solver：指代用的什么方法\n",
    "# max_iter:最大迭代次数\n",
    "#由于该数据集是线性不可分的\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "models.append((\"GBTD\",GradientBoostingClassifier(max_depth=6,n_estimators=100)))\n",
    "#n_estimatorsx进行迭代的弱分类器的个数\n",
    "for clf_name,clf in models:\n",
    "    clf.fit(x_tra,y_tra)\n",
    "    xy_lst=[(x_tra,y_tra),(x_tes,y_tes),(x_val,y_val)]\n",
    "    for i in range(len(xy_lst)):\n",
    "        X_part=xy_lst[i][0]\n",
    "        y_part=xy_lst[i][1]\n",
    "        Y_pred=clf.predict(X_part)\n",
    "        print('............'+clf_name+'....'+str(i)+'..........')       \n",
    "        print(\"Acc:\",accuracy_score(y_part,Y_pred))\n",
    "        print(\"Recall:\",recall_score(y_part,Y_pred))\n",
    "        print(\"F1_score:\",f1_score(y_part,Y_pred))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [],
   "source": [
    "#朴素贝叶斯，生成模型，要求联合概率分布\n",
    "from sklearn.naive_bayes import GaussianNB,BernoulliNB#高斯朴素贝叶斯(大多数是离散的)，伯路利朴素贝叶斯（二值，这个更好）\n",
    "#特征必须是离散的\n",
    "models.append((\"GaussianNB\",GaussianNB()))\n",
    "models.append((\"BernoulliNB\",BernoulliNB()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [],
   "source": [
    "#决策树\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "models.append((\"DecisionTreeClassifier\",DecisionTreeClassifier()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [],
   "source": [
    "#svm分类器\n",
    "# 找出最近两个点离分界面最近，之间距离最大的两个点\n",
    "from sklearn.svm import SVC\n",
    "model.append((\"SVM classifier\",SVC(C=1000)))\n",
    "#可以查看官网查看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [],
   "source": [
    "#集成方法\n",
    "# 组合多个模型，以获得更好的结果\n",
    "# ---随机森林:\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "models.append((\"RandForetClassifier\",RandomForestClassifier()))\n",
    "#https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [],
   "source": [
    "#集成方法\n",
    "#Adaboost\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "models.append((\"AdaBoostClassifier\",AdaBoostClassifier()))\n",
    "#默认弱分类器是决策树\n",
    "#https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conf [0.26153935 0.25572149]\n",
      "MSE 0.05956259739891546\n",
      "MAE 0.20506719465294695\n",
      "R2 0.1672555561165544\n"
     ]
    }
   ],
   "source": [
    "#回归模型：Ridge岭回归回归：有序定序数据的分类）\n",
    "def regr_tes(features,label):\n",
    "#     print(\"X\",features)\n",
    "#     print(\"Y\",label)\n",
    "    from sklearn.linear_model import LinearRegression,Ridge,Lasso\n",
    "    \n",
    "    regr=Lasso(alpha=0.001)#lasso回归\n",
    "    \n",
    "#   regr=Ridge(alpha=0.5)#岭回归\n",
    "#   regr=LinearRegression()#线性回归\n",
    "    regr.fit(features.values,label.values)\n",
    "    Y_pre=regr.predict(features.values)\n",
    "    print(\"conf\",regr.coef_)\n",
    "    from sklearn.metrics import mean_squared_error,mean_absolute_error,r2_score\n",
    "    print(\"MSE\",mean_squared_error(Y_pre,label.values))\n",
    "    print(\"MAE\",mean_absolute_error(label.values,Y_pre))\n",
    "    print(\"R2\",r2_score(label.values,Y_pre))\n",
    "regr_tes(features[[\"number_project\",\"average_montly_hours\"]],features[\"last_evaluation\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [],
   "source": [
    "#罗吉斯特回归（逻辑回归）（回归：有序定序数据的分类）#非线性\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "models.append((\"LogisticRegression\",LogisticRegression()))\n",
    "#https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'keras'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-438-ce83332bfacc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#人工神经网络\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mDense\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mActivation\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'keras'"
     ]
    }
   ],
   "source": [
    "#人工神经网络\n",
    "from keras.layers.core import Dense,Activation\n",
    "............."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [],
   "source": [
    "#回归树和GBTD\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "models.append((\"GBTD\",GradientBoostingClassifier()))\n",
    "#https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 无监督的学习--聚类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([[0.50415247, 0.55278607],\n",
      "       [0.48171368, 0.95194511],\n",
      "       [0.27113113, 0.21000169],\n",
      "       ...,\n",
      "       [0.67660517, 0.06867743],\n",
      "       [0.05660962, 0.96504031],\n",
      "       [0.10610222, 0.15743002]]), None)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_circles,make_blobs,make_moons\n",
    "n_samples=1000\n",
    "circle=make_circles(n_samples=n_samples,factor=0.5,noise=0.05)\n",
    "moons=make_moons(n_samples=n_samples,noise=0.05)\n",
    "blobs=make_blobs(n_samples=n_samples,random_state=8)\n",
    "random_data=np.random.rand(n_samples,2),None\n",
    "\n",
    "print(random_data)\n",
    "f=plt.figure()\n",
    "# print(circle)\n",
    "colors=\"bgrcmyk\"\n",
    "data=[circle,moons,blobs,random_data]\n",
    "models=[(\"None\",None)]\n",
    "for inx,clt in enumerate(models):\n",
    "    clt_name,clt_entity=clt\n",
    "    for i,dataset in enumerate(data):\n",
    "        X,Y=dataset\n",
    "        if not clt_entity:\n",
    "            clt_res=[0 for item in range(len(X))]\n",
    "            f.add_subplot(len(models),len(data),inx*len(data)+i+1)\n",
    "            [plt.scatter(X[p,0],X[p,1],color=colors[clt_res[p]]) for p in range(len(X))]\n",
    "plt.show()\n",
    "\n",
    "#数据集的形状\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KMeans 0 0.38918353357379804\n",
      "KMeans 1 0.4279218214804981\n",
      "KMeans 2 0.8260921886020176\n",
      "KMeans 3 0.381199609507512\n",
      "DBSCAN 0 0.1145459300134317\n",
      "DBSCAN 1 0.3354305494605702\n",
      "DBSCAN 2 0.8260921886020176\n",
      "Agglomerative 0 0.3513324845174509\n",
      "Agglomerative 1 0.41533681377431764\n",
      "Agglomerative 2 0.8260921886020176\n",
      "Agglomerative 3 0.3057645483624013\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#K_means结果\n",
    "from sklearn.datasets import make_circles,make_blobs,make_moons\n",
    "#制作数据集\n",
    "from sklearn.cluster import KMeans\n",
    "n_samples=1000\n",
    "circle=make_circles(n_samples=n_samples,factor=0.5,noise=0.05)\n",
    "moons=make_moons(n_samples=n_samples,noise=0.05)\n",
    "blobs=make_blobs(n_samples=n_samples,random_state=8,center_box=(-1,1),cluster_std=(0.1))\n",
    "\n",
    "random_data=np.random.rand(n_samples,2),None\n",
    "f=plt.figure()\n",
    "# print(circle)\n",
    "colors=\"bgrcmyk\"\n",
    "data=[circle,moons,blobs,random_data]\n",
    "models=[(\"None\",None),(\"KMeans\",KMeans(n_clusters=3))]\n",
    "from sklearn.cluster import DBSCAN\n",
    "models.append((\"DBSCAN\",DBSCAN(min_samples=3,eps=0.2)))\n",
    "#eps:E领域，min_samples最小的点集\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "models.append((\"Agglomerative\",AgglomerativeClustering(n_clusters=3,linkage=\"ward\")))\n",
    "from sklearn.metrics import silhouette_score#引入轮廓系数\n",
    "\n",
    "for inx,clt in enumerate(models):\n",
    "    clt_name,clt_entity=clt\n",
    "    for i,dataset in enumerate(data):\n",
    "        X,Y=dataset\n",
    "        if not clt_entity:\n",
    "            clt_res=[0 for item in range(len(X))]\n",
    "        else:\n",
    "            clt_entity.fit(X)\n",
    "            clt_res=clt_entity.labels_.astype(np.int)\n",
    "        f.add_subplot(len(models),len(data),inx*len(data)+i+1)\n",
    "        try:\n",
    "            print(clt_name,i,silhouette_score(X,clt_res))\n",
    "        except:\n",
    "            pass\n",
    "        [plt.scatter(X[p,0],X[p,1],color=colors[clt_res[p]]) for p in range(len(X))]\n",
    "plt.show()\n",
    "# https://scikit-learn.org/stable/datasets/index.html\n",
    "#数据集的形状"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [],
   "source": [
    "#DBSCAN聚类算法\n",
    "from sklearn.cluster import DBSCAN\n",
    "models.append((\"DBSCAN\",DBSCAN(min_samples=3,eps=0.5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {},
   "outputs": [],
   "source": [
    "#层次聚类\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "models.append((\"Agglomerative\",AgglomerativeClustering()))\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 无监督学习--关联规则"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {},
   "outputs": [],
   "source": [
    "#关联规则\n",
    "#序列模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 半监督学习\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unlabeled Number: 49\n",
      "Acc: 0.9795918367346939\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Target is multiclass but average='binary'. Please choose another average setting.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-497-ff17eb96f513>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     28\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrecall_score\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mf1_score\u001b[0m\u001b[1;31m#加载评价的标准\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     29\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Acc:\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maccuracy_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mY_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 30\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Recall:\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrecall_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mY_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     31\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"F1_score:\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mf1_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mY_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anacondainstall\\lib\\site-packages\\sklearn\\metrics\\classification.py\u001b[0m in \u001b[0;36mrecall_score\u001b[1;34m(y_true, y_pred, labels, pos_label, average, sample_weight)\u001b[0m\n\u001b[0;32m   1357\u001b[0m                                                  \u001b[0maverage\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maverage\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1358\u001b[0m                                                  \u001b[0mwarn_for\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'recall'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1359\u001b[1;33m                                                  sample_weight=sample_weight)\n\u001b[0m\u001b[0;32m   1360\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1361\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anacondainstall\\lib\\site-packages\\sklearn\\metrics\\classification.py\u001b[0m in \u001b[0;36mprecision_recall_fscore_support\u001b[1;34m(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight)\u001b[0m\n\u001b[0;32m   1038\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1039\u001b[0m             raise ValueError(\"Target is %s but average='binary'. Please \"\n\u001b[1;32m-> 1040\u001b[1;33m                              \"choose another average setting.\" % y_type)\n\u001b[0m\u001b[0;32m   1041\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mpos_label\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1042\u001b[0m         warnings.warn(\"Note that pos_label (set to %r) is ignored when \"\n",
      "\u001b[1;31mValueError\u001b[0m: Target is multiclass but average='binary'. Please choose another average setting."
     ]
    }
   ],
   "source": [
    "#标签传播\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.semi_supervised.LabelPropagation.html\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn  import datasets\n",
    "iris=datasets.load_iris()\n",
    "labels=np.copy(iris.target)\n",
    "random_unlabel=np.random.rand(len(iris.target))\n",
    "random_unlabel=random_unlabel<0.3  #<0.3返回1，否则返回0\n",
    "\n",
    "Y=labels[random_unlabel]\n",
    "# print(Y)\n",
    "# print(\"————————————————————————————\")\n",
    "labels[random_unlabel]=-1\n",
    "# print(iris.target)\n",
    "# print(labels)\n",
    "# print(len(labels))\n",
    "print(\"Unlabeled Number:\",list(labels).count(-1))\n",
    "\n",
    "from sklearn.semi_supervised import LabelPropagation\n",
    "label_prop_model=LabelPropagation()\n",
    "label_prop_model.fit(iris.data,labels)\n",
    "\n",
    "Y_pred=label_prop_model.predict(iris.data)\n",
    "Y_pred=Y_pred[random_unlabel]\n",
    "from sklearn.metrics import accuracy_score,recall_score,f1_score#加载评价的标准\n",
    "print(\"Acc:\",accuracy_score(Y,Y_pred))\n",
    "print(\"Recall:\",recall_score(Y,Y_pred))\n",
    "print(\"F1_score:\",f1_score(Y,Y_pred))\n",
    "# 这个问题的解决的方法：可以在https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html\n",
    "#  找到，修改在下行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unlabeled Number: 44\n",
      "Acc: 0.9318181818181818\n",
      "Recall: 0.9318181818181818\n",
      "F1_score: 0.9318181818181818\n"
     ]
    }
   ],
   "source": [
    "#标签传播\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.semi_supervised.LabelPropagation.html\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn  import datasets\n",
    "iris=datasets.load_iris()\n",
    "labels=np.copy(iris.target)\n",
    "random_unlabel=np.random.rand(len(iris.target))\n",
    "random_unlabel=random_unlabel<0.3  #<0.3返回1，否则返回0\n",
    "\n",
    "Y=labels[random_unlabel]\n",
    "# print(Y)\n",
    "# print(\"————————————————————————————\")\n",
    "labels[random_unlabel]=-1\n",
    "# print(iris.target)\n",
    "# print(labels)\n",
    "# print(len(labels))\n",
    "print(\"Unlabeled Number:\",list(labels).count(-1))\n",
    "\n",
    "from sklearn.semi_supervised import LabelPropagation\n",
    "label_prop_model=LabelPropagation()\n",
    "label_prop_model.fit(iris.data,labels)\n",
    "\n",
    "Y_pred=label_prop_model.predict(iris.data)\n",
    "Y_pred=Y_pred[random_unlabel]\n",
    "from sklearn.metrics import accuracy_score,recall_score,f1_score#加载评价的标准\n",
    "print(\"Acc:\",accuracy_score(Y,Y_pred))\n",
    "print(\"Recall:\",recall_score(Y,Y_pred,average='micro'))\n",
    "print(\"F1_score:\",f1_score(Y,Y_pred,average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sklearn的网络\n",
    "#https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#评价指标\n",
    "#https://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics"
   ]
  }
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